U.S. patent application number 13/479546 was filed with the patent office on 2013-02-14 for playing digital content from radio-transmitted media based on taste profiles.
This patent application is currently assigned to EMERGENT DISCOVERY LLC. The applicant listed for this patent is Gary Robinson. Invention is credited to Gary Robinson.
Application Number | 20130040556 13/479546 |
Document ID | / |
Family ID | 34831205 |
Filed Date | 2013-02-14 |
United States Patent
Application |
20130040556 |
Kind Code |
A1 |
Robinson; Gary |
February 14, 2013 |
PLAYING DIGITAL CONTENT FROM RADIO-TRANSMITTED MEDIA BASED ON TASTE
PROFILES
Abstract
A portable content-playing device which automatically constructs
a virtual channel of content consistent with the tastes of the user
of the device, where the content originates from one or more
satellites. The portable device contains software for computing
similarity values between a local taste profile (representative of
the songs tastes of the user of the device) by making use of
downloaded pattern-matching technology representing each song, and
this information is used in choosing the content of the virtual
channel.
Inventors: |
Robinson; Gary; (Bangor,
ME) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Robinson; Gary |
Bangor |
ME |
US |
|
|
Assignee: |
EMERGENT DISCOVERY LLC
Portland
ME
|
Family ID: |
34831205 |
Appl. No.: |
13/479546 |
Filed: |
May 24, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12806856 |
Aug 23, 2010 |
8190082 |
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13479546 |
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11974634 |
Oct 15, 2007 |
7783249 |
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12806856 |
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11230274 |
Sep 19, 2005 |
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11974634 |
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PCT/US05/02731 |
Jan 27, 2005 |
|
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11230274 |
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60540041 |
Jan 27, 2004 |
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60611222 |
Sep 18, 2004 |
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60635197 |
Dec 9, 2004 |
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Current U.S.
Class: |
455/3.02 |
Current CPC
Class: |
G06F 15/16 20130101;
H04L 51/32 20130101; H04L 12/185 20130101; G06Q 10/00 20130101;
H04L 12/4625 20130101; G06Q 10/107 20130101; G06Q 30/02 20130101;
H04L 12/00 20130101; H04L 12/1827 20130101; H04L 51/00
20130101 |
Class at
Publication: |
455/3.02 |
International
Class: |
H04H 20/74 20080101
H04H020/74 |
Claims
1. A portable content-playing device for playing digital content
communicated along with related data from a satellite or other
one-way means of transmission of digital data, comprising: radio
communication hardware for receiving data from said satellite or
other one-way means of transmission of digital data; a central
processing unit and supporting hardware including random access
memory and persistent storage, said persistent storage storing data
including locally-stored digital content and taste profile data and
software programs, said taste profile data including a local taste
profile representing the tastes associated with (a) active
indicators of taste entered manually by the user of the device, or
(b) passive indicators of taste derived from data representing the
user's actions while controlling playback, or (c) active and
passive indicators of taste, said indicators indicating the tastes
of a user of said portable content-playing-device as to various (A)
songs, or (B) videos, or (C) spoken content, or (D) any combination
of the foregoing, said software programs including: (i) a software
program for executing pattern-matching technologies to compute
similarity values between said local taste profile and attributes
associated with one or more (A) songs, or (B) videos, or (C) spoken
content, or (D) any combination of the foregoing, and (ii) a
software program for identifying which digital content communicated
from said satellite or other one-way means of transmission of
digital data to play for said user in accordance with said
similarity values computed with respect to said local taste
profile, whereby a virtual channel is constructed automatically by
inference from said local taste profile, providing said user the
experience of a digital content channel specifically geared towards
that individual user's taste profile.
2. The portable content-playing device of claim 1, wherein said
pattern-matching technologies comprise a plurality of neural nets,
each one of said neural nets corresponding to (a) a song, or (b) a
video, or (c) an item of spoken content.
3. The portable content-playing device of claim 1, further
comprising a save button enabling the user to indicate that the
user wishes to save the digital content item being played at the
time the button is pressed so that it may played again later, and
software responsive to activation of said save button for
generating an element of said local taste profile as a passive
indicator that the user likes the digital content item being played
at said time.
4. The portable content-playing device of claim 1, further
comprising: hardware responsive to command by the user so as to
selectively cause the device to save a particular item of content
or to replay a particular item of content; said software programs
further including: software for generating elements of said local
taste profile from passive indicators of taste of the user,
generated upon the user's (a) causing the device to save a
particular item of content; or (b) causing the device to replay a
particular item of content; wherein said software causes to be
stored in said taste profile data indicating that the user likes
said particular item of content.
5. The portable content-playing device of claim 1, further
comprising: hardware responsive to command by the user so as to
selectively cause the device to skip all or part of a particular
item of content; said software programs further including: software
for generating elements of said local taste profile from passive
indicators of taste of the user, generated upon the user's causing
the device to skip all or part of a particular item of content;
wherein said software causes to be stored in said taste profile
data indicating that the user does not like said particular item of
content.
6. The portable content-playing device of claim 1, said software
programs further including: software for generating elements of
said local taste profile from active indicators of taste of the
user, generated upon the user's activating a sensor on the device
that the user likes a particular item of content wherein said
software causes to be stored in said taste profile data indicating
that the user likes said particular item of content.
7. The portable content-playing device of claim 1, said software
programs further including: software for generating elements of
said local taste profile from active indicators of taste of the
user, generated upon the user's activating a sensor on the device
that the user dislikes a particular item of content wherein said
software causes to be stored in said taste profile data indicating
that the user does not like said particular item of content.
8. A portable content-playing device for playing digital content
communicated along with related data from at least one satellite,
comprising radio communication hardware for receiving data from
said at least one satellite, a central processing unit and
supporting hardware including random access memory and persistent
storage, said persistent storage storing data including
locally-stored digital content and taste profile data and software
programs, said taste profile data including a local taste profile
comprising identifiers of content items liked by a user of said
portable content-playing-device, each of said identifiers having
associated therewith data corresponding to one or more attributes
pertaining to said content item, said software programs including:
software for downloading lists of identifiers of content items and
their associated attributes from said at least one satellite, and
software for computing the similarity of the attributes associated
with each of said content items on said lists with said local taste
profile and recommending the identifier associated with those of
said content items for which the results of such computations have
similarity to the local taste profile; and software for acquiring
and playing within a channel on said portable content-playing
device a plurality of the content items associated with said
recommended identifiers, whereby a virtual channel is constructed
automatically by inference from said local taste profile and
identifiers of content items by acquiring and playing the content
items associated with said identifiers for the benefit of said
user, providing said user the experience of a digital content
channel specifically geared towards that individual user's taste
profile.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 12/806,856, filed 23 Aug. 2010 (U.S. Pat. No.
8,190,082), which is a continuation of U.S. patent application Ser.
No. 11/974,634 filed 15 Oct. 2007 (U.S. Pat. No. 7,783,249), which
is a divisional of U.S. patent application Ser. No. 11/230,274,
filed 19 Sep. 2005; which is a continuation-in-part of
International Patent Application: PCT/US2005/02731, filed 27 Jan.
2005 for Enabling Recommendations and Community By
Massively-Distributed Nearest-Neighbor Searching, which claims
priority from and benefit of the following U.S. Provisional Patent
Applications 60/540,041 filed 27 Jan. 2004, for Enabling
Recommendations and Community by Massively-Distributed
Nearest-Neighbor Searching; 60/611,222 filed 18 Sep. 2004 for
Community and Recommendation System; and 60/635,197 filed 9 Dec.
2004 for Community and Recommendation System. Applicant hereby
claims priority from and benefit of the aforesaid applications
60/611,222 and 60/635,197. Applicant hereby incorporates by
reference herein to the fullest extent allowed by law the entire
disclosure of each of the aforesaid applications, including all
text, drawings, and code whether on paper or machine-readable
media.
RESERVATION OF COPYRIGHT
Copyright .COPYRGT. 2003, 2004, 2005 Emergent Discovery LLC
[0002] This application includes material which is subject to
copyright protection. The copyright owner has no objection to the
facsimile reproduction by anyone of the patent disclosure as it
appears in patent office files or records, but otherwise reserves
all copyright rights whatsoever.
COMPACT DISC INCORPORATION BY REFERENCE
[0003] Applicant hereby incorporates by reference the entire
contents of the material on the compact discs submitted by Express
Mail concurrently herewith, and as listed below. The discs' content
was originally copied to CD ROM on 17 Sep. 2005, from files created
on the various dates stated below. Applicants submit herewith two
compact discs generated 24 May 2012, each being identical to the
other, except for being labeled as Copy 1 and Copy 2.
TABLE-US-00001 Size Mon Day Year File ./cooltunes/Goombah Help:
3358 Aug 31 2004 bittorrent.html 3455 Jul 14 2004 blogging.html
3077 Aug 31 2004 contacting.html 2621 Aug 31 2004 index.html 2945
Aug 31 2004 install.html 3097 Jul 14 2004 neighbors.html 2842 Jul
14 2004 playingmusic.html 4019 Aug 31 2004 prefs.html 3086 Aug 31
2004 profiles.html 3046 Jul 14 2004 recommendations.html 2893 Jul
14 2004 suggestions.html 2819 Jul 14 2004 terms.html 2665 Jul 14
2004 upgrades.html 5738 Jul 27 2004 web.html 3145 Jul 14 2004
whatisit.html ./cooltunes/Goombah Help/images: (empty)
./cooltunes/bittorrent: 6255 Sep 17 2004 bittorrentfetcherclass.py
2001 Sep 17 2004 bittorrentfetcherclasstest.py 1769 Jul 15 2004
bittorrenttimedfetcherclass.py 1705 Jul 15 2004
bittorrenttimedfetcherclasstest.py 2022 Aug 30 2004
btcreatetorrentsmain.py 5061 Aug 30 2004 btdirectoryclass.py 1112
Aug 30 2004 btdirectoryclassmain.py 10396 Aug 30 2004
btdirectoryclasstest.py 4786 Sep 2 2004 btseedmanagerclass.py 5462
Aug 30 2004 btseedmanagerclasstest.py 2346 Aug 11 2004
btseedsdaemonmain.py 1149 Aug 11 2004 bttrackdaemonmain.py
./cooltunes/clustering: 7780 Aug 20 2004 clusterbuilderclass.py
1877 Aug 26 2004 clusterbuilderclassmain.py 7512 Aug 20 2004
clusterbuilderclasstest.py 17139 Jul 13 2004 clusterfitterclass.py
12368 Jul 13 2004 clusterfitterclasstest.py 1876 Aug 13 2004
clusteringcandidatesfileclass.py 4060 Jun 9 2004
clusteringcandidatesfileclasstest.py 1835 Jun 9 2004 extremes.py
38421 Jul 16 2004 genrerankhandlerclass.py 10305 Jul 13 2004
genrerankhandlerclassrefactorings.txt 102592 Jul 13 2004
genrerankhandlerclasstest.py 1778 Jun 2 2004
publicprofilestringclass.py 1089 Jun 2 2004
publicprofilestringclasstest.py 2315 Jun 9 2004 unique.py
./cooltunes/getblogurl: 5715 Jul 26 2004 getblogurlclass.py
./cooltunes/initialcandidates: 421 Jun 22 2004
ReadMe.Candidates.txt ./cooltunes/libchanges/BitTorrent: 47 Jun 3
2004 BitTorrentVersion-3.4.2.txt 9955 Jun 3 2004 Choker.py 10802
Jun 3 2004 Connecter.py 1016 Jun 3 2004 CurrentRateMeasure.py 17734
Jun 3 2004 Downloader.py 2855 Jun 3 2004 DownloaderFeedback.py
18789 Jun 3 2004 Encrypter.py 6054 Jun 3 2004 HTTPHandler.py 2650
Jun 3 2004 NatCheck.py 5014 Jun 3 2004 PiecePicker.py 1503 Jun 3
2004 RateMeasure.py 18347 Jun 3 2004 RawServer.py 5653 Jun 3 2004
Rerequester.py 5021 Jun 3 2004 Storage.py 17029 Jun 3 2004
StorageWrapper.py 8059 Jun 3 2004 Uploader.py 18 Jun 3 2004
_init_.py 7052 Jun 3 2004 bencode.py 3733 Jun 3 2004 bitfield.py
3831 Jun 3 2004 btformats.py 12791 Jun 3 2004 download.py 2240 Jun
3 2004 fakeopen.py 3579 Jun 3 2004 parseargs.py 2287 Jun 3 2004
selectpoll.py 2052 Jun 3 2004 testtest.py 24605 Jun 3 2004 track.py
4261 Jun 3 2004 zurllib.py ./cooltunes/libchanges/macos/xml: 1806
Apr 5 2004 FtCore.py 389 Apr 5 2004 ReadMe.Bob.txt 1083 Apr 5 2004
_init_.py 9627 Apr 5 2004 ns.py
./cooltunes/libchanges/macos/xml/dom: 4235 Apr 5 2004 Attr.py 644
Apr 5 2004 CDATASection.py 4094 Apr 5 2004 CharacterData.py 603 Apr
5 2004 Comment.py 1936 Apr 5 2004 DOMImplementation.py 11948 Apr 5
2004 Document.py 1296 Apr 5 2004 DocumentFragment.py 3399 Apr 5
2004 DocumentType.py 10264 Apr 5 2004 Element.py 2610 Apr 5 2004
Entity.py 1394 Apr 5 2004 EntityReference.py 3438 Apr 5 2004
Event.py 16628 Apr 5 2004 FtNode.py 2259 Apr 5 2004
MessageSource.py 5052 Apr 5 2004 NamedNodeMap.py 937 Apr 5 2004
NodeFilter.py 3998 Apr 5 2004 NodeIterator.py 1442 Apr 5 2004
NodeList.py 2056 Apr 5 2004 Notation.py 2080 Apr 5 2004
ProcessingInstruction.py 40190 Apr 5 2004 Range.py 1195 Apr 5 2004
Text.py 6995 Apr 5 2004 TreeWalker.py 7545 Apr 5 2004 _init_.py
3481 Apr 5 2004 domreg.py 36379 Apr 5 2004 expatbuilder.py 19289
Apr 5 2004 javadom.py 5287 Apr 5 2004 minicompat.py 65671 Apr 5
2004 minidom.py 1274 Apr 5 2004 minitraversal.py 11978 Apr 5 2004
pulldom.py 12384 Apr 5 2004 xmlbuilder.py
./cooltunes/libchanges/macos/xml/dom/ext: 11057 Apr 5 2004
Dom2Sax.py 13835 Apr 5 2004 Printer.py 2344 Apr 5 2004 Visitor.py
1584 Apr 5 2004 XHtml2HtmlPrinter.py 1634 Apr 5 2004
XHtmlPrinter.py 10102 Apr 5 2004 _init_.py 13186 Apr 5 2004 c14n.py
./cooltunes/libchanges/macos/xml/dom/ext/reader: 3174 Apr 5 2004
HtmlLib.py 3123 Apr 5 2004 HtmlSax.py 8871 Apr 5 2004 PyExpat.py
6381 Apr 5 2004 Sax.py 15985 Apr 5 2004 Sax2.py 8295 Apr 5 2004
Sax2Lib.py 10310 Apr 5 2004 Sgmlop.py 2207 Apr 5 2004 _init_.py
./cooltunes/libchanges/macos/xml/dom/html: 9836 Apr 5 2004
GenerateHtml.py 3788 Apr 5 2004 HTMLAnchorElement.py 3411 Apr 5
2004 HTMLAppletElement.py 2959 Apr 5 2004 HTMLAreaElement.py 1309
Apr 5 2004 HTMLBRElement.py 1501 Apr 5 2004 HTMLBaseElement.py 1702
Apr 5 2004 HTMLBaseFontElement.py 2361 Apr 5 2004
HTMLBodyElement.py 2686 Apr 5 2004 HTMLButtonElement.py 2175 Apr 5
2004 HTMLCollection.py 1396 Apr 5 2004 HTMLDListElement.py 1047 Apr
5 2004 HTMLDOMImplementation.py 1405 Apr 5 2004
HTMLDirectoryElement.py 1509 Apr 5 2004 HTMLDivElement.py 11633 Apr
5 2004 HTMLDocument.py 3572 Apr 5 2004 HTMLElement.py 1299 Apr 5
2004 HTMLFieldSetElement.py 1690 Apr 5 2004 HTMLFontElement.py 3327
Apr 5 2004 HTMLFormElement.py 3564 Apr 5 2004 HTMLFrameElement.py
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HTMLHRElement.py 1312 Apr 5 2004 HTMLHeadElement.py 1314 Apr 5 2004
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2004 HTMLIFrameElement.py 3888 Apr 5 2004 HTMLImageElement.py 6481
Apr 5 2004 HTMLInputElement.py 1553 Apr 5 2004
HTMLIsIndexElement.py 1558 Apr 5 2004 HTMLLIElement.py 1784 Apr 5
2004 HTMLLabelElement.py 1798 Apr 5 2004 HTMLLegendElement.py 3046
Apr 5 2004 HTMLLinkElement.py 1377 Apr 5 2004 HTMLMapElement.py
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HTMLMetaElement.py 1514 Apr 5 2004 HTMLModElement.py 1869 Apr 5
2004 HTMLOListElement.py 5233 Apr 5 2004 HTMLObjectElement.py 1623
Apr 5 2004 HTMLOptGroupElement.py 3651 Apr 5 2004
HTMLOptionElement.py 1322 Apr 5 2004 HTMLParagraphElement.py 1949
Apr 5 2004 HTMLParamElement.py 1364 Apr 5 2004 HTMLPreElement.py
1283 Apr 5 2004 HTMLQuoteElement.py 3150 Apr 5 2004
HTMLScriptElement.py 4750 Apr 5 2004 HTMLSelectElement.py 1811 Apr
5 2004 HTMLStyleElement.py 1334 Apr 5 2004
HTMLTableCaptionElement.py 4684 Apr 5 2004 HTMLTableCellElement.py
2421 Apr 5 2004 HTMLTableColElement.py 9117 Apr 5 2004
HTMLTableElement.py 3711 Apr 5 2004 HTMLTableRowElement.py 2877 Apr
5 2004 HTMLTableSectionElement.py 4989 Apr 5 2004
HTMLTextAreaElement.py 1837 Apr 5 2004 HTMLTitleElement.py 1612 Apr
5 2004 HTMLUListElement.py 36479 Apr 5 2004 _init_.py
./cooltunes/libchanges/macos/xml/marshal: 359 Apr 5 2004 _init_.py
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./cooltunes/libchanges/macos/xml/parsers: 43 Apr 5 2004 _init_.py
116 Apr 5 2004 expat.py 19361 Apr 5 2004 sgmllib.py
./cooltunes/libchanges/macos/xml/parsers/xmlproc: 22 Apr 5 2004
_init_.py 1657 Apr 5 2004 _outputters.py 10134 Apr 5 2004
catalog.py 6593 Apr 5 2004 charconv.py 22875 Apr 5 2004
dtdparser.py 33805 Apr 5 2004 errors.py 4852 Apr 5 2004
namespace.py 6752 Apr 5 2004 utils.py 2340 Apr 5 2004 xcatalog.py
7067 Apr 5 2004 xmlapp.py 28475 Apr 5 2004 xmldtd.py 19970 Apr 5
2004 xmlproc.py 32619 Apr 5 2004 xmlutils.py 10167 Apr 5 2004
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expatreader.py 14084 Apr 5 2004 handler.py 1250 Apr 5 2004
sax2exts.py 6617 Apr 5 2004 saxexts.py 15687 Apr 5 2004 saxlib.py
24428 Apr 5 2004 saxutils.py 18864 Apr 5 2004 writer.py 12580 Apr 5
2004 xmlreader.py ./cooltunes/libchanges/macos/xml/sax/drivers: 39
Apr 5 2004 _init_.py 1051 Apr 5 2004 drv_htmllib.py 3112 Apr 5 2004
drv_ltdriver.py 895 Apr 5 2004 drv_ltdriver_val.py 5893 Apr 5 2004
drv_pyexpat.py 979 Apr 5 2004 drv_sgmllib.py 2700 Apr 5 2004
drv_sgmlop.py 3685 Apr 5 2004 drv_xmldc.py 2709 Apr 5 2004
drv_xmllib.py 4402 Apr 5 2004 drv_xmlproc.py 1774 Apr 5 2004
drv_xmlproc_val.py 2509 Apr 5 2004 drv_xmltoolkit.py 3393 Apr 5
2004 pylibs.py ./cooltunes/libchanges/macos/xml/sax/drivers2: 39
Apr 5 2004 _init_.py 422 Apr 5 2004 drv_htmllib.py 5931 Apr 5 2004
drv_javasax.py 645 Apr 5 2004 drv_pyexpat.py 3759 Apr 5 2004
drv_sgmllib.py 4386 Apr 5 2004 drv_sgmlop.py 2467 Apr 5 2004
drv_sgmlop_html.py
13532 Apr 5 2004 drv_xmlproc.py
./cooltunes/libchanges/macos/xml/schema: 38 Apr 5 2004 _init_.py
60039 Apr 5 2004 trex.py ./cooltunes/libchanges/macos/xml/unicode:
158 Apr 5 2004 _init_.py 2863 Apr 5 2004 iso8859.py 11690 Apr 5
2004 utf8_iso.py ./cooltunes/libchanges/macos/xml/utils: 22 Apr 5
2004 _init_.py 26221 Apr 5 2004 characters.py 5676 Apr 5 2004
iso8601.py 6160 Apr 5 2004 qp_xml.py
./cooltunes/libchanges/macos/xml/xpath: 9457 Apr 5 2004
BuiltInExtFunctions.py 2193 Apr 5 2004 Context.py 5865 Apr 5 2004
Conversions.py 11233 Apr 5 2004 CoreFunctions.py 1159 Apr 5 2004
ExpandedNameWrapper.py 996 Apr 5 2004 MessageSource.py 757 Apr 5
2004 NamespaceNode.py 2047 Apr 5 2004
ParsedAbbreviatedAbsoluteLocationPath.py 2137 Apr 5 2004
ParsedAbbreviatedRelativeLocationPath.py 1228 Apr 5 2004
ParsedAbsoluteLocationPath.py 9080 Apr 5 2004
ParsedAxisSpecifier.py 21415 Apr 5 2004 ParsedExpr.py 5443 Apr 5
2004 ParsedNodeTest.py 2483 Apr 5 2004 ParsedPredicateList.py 1464
Apr 5 2004 ParsedRelativeLocationPath.py 3414 Apr 5 2004
ParsedStep.py 951 Apr 5 2004 Set.py 6005 Apr 5 2004 Util.py 34402
Apr 5 2004 XPathGrammar.py 37104 Apr 5 2004 XPathParser.py 2924 Apr
5 2004 XPathParserBase.py 3192 Apr 5 2004 _init_.py 11280 Apr 5
2004 pyxpath.py 6236 Apr 5 2004 yappsrt.py ./cooltunes/macui: 3993
Apr 5 2004 Building_coolTunes.txt 528 Apr 5 2004 Credits.html 2142
May 10 2004 alertdialogclasstestmanually.py 3022 Jul 26 2004
buildapp.py 144 May 10 2004 buildapp_alertdialgoclasstestscript.py
171 Jul 21 2004 buildapp_macpleasewaitdialogclasstestscript.py 143
Jun 23 2004 buildapp_opendialogclasstestscript.py 164 Apr 16 2004
buildapp_progressdialogclasstestscript.py 4232 Jun 22 2004
builddiskimage.sh 310 Apr 5 2004 clearuser.sh 134519 Sep 17 2004
cooltunescontrollerclass.py 277 Apr 6 2004
cooltunescontrollerclasstest.py 763 Jul 26 2004 cooltunesmain.py
6491 Apr 16 2004 itunesdbreaderclass.py 845 Jul 26 2004
itunesdbreaderprogressslavemain.py 10655 Sep 13 2004
itunesdbreaderslaveclass.py 859 Jul 26 2004
itunesdbreaderslavemain.py 3183 Apr 5 2004 itunesscripterclass.py
1134 May 10 2004 macalertdialogclass.py 1009 Jun 23 2004
macopendialogclass.py 3874 Jul 21 2004 macpleasewaitdialogclass.py
1644 Aug 20 2004 macpleasewaitdialogclasstest.py 5411 May 10 2004
macprogressdialogclass.py 1110 Apr 16 2004 nibutilities.py 1533 Jun
23 2004 opendialogclasstestmanually.py 13767 Jul 26 2004
preffileclass.py 1189 Jun 4 2004 preffileclasstest.py 1595 Apr 16
2004 progressdialogclasstest.py ./cooltunes/patterns: 1497 Jun 24
2004 immutablelistclass.py 6695 Apr 5 2004 observermixin.py 8089
Apr 5 2004 older 8143 Apr 5 2004 persistencemixin.py 4347 Apr 5
2004 singletonautopersistence.py 8642 Jun 10 2004 singletonmixin.py
4355 Apr 5 2004 synchronization.py ./cooltunes/pyclient: 850 Jun 11
2004 alertdialogclass.py 6829 Jul 23 2004 candidatefileclass.py
11120 Aug 20 2004 candidatefileclasstest.py 12624 Sep 17 2004
candidatefilefetcherclass.py 9823 Sep 16 2004
candidatefilefetcherclasstest.py 1869 May 13 2004
candidatefileneighborretrieverclass.py 3502 May 13 2004
candidatefileneighborretrieverclasstest.py 397 Jul 23 2004
clientemail.py 593 Jul 23 2004 clientemailtest.py 42266 Sep 17 2004
cooltunesclass.py 15103 Jul 29 2004 cooltunesclasstest.py 26 Jun 22
2004 cooltunesversion.py 3578 Apr 6 2004
currentclientversionclass.py 3899 Apr 14 2004
currentclientversionclasstest.py 5476 Aug 10 2004 daemonize.py 4235
Aug 20 2004 errorloggerclass.py 3000 Apr 14 2004
errorloggerclasstest.py 3084 Aug 2 2004 filteredrecommenderclass.py
4745 Aug 2 2004 filteredrecommenderclasstest.py 1042 Jun 9 2004
genreprofilerclass.py 1207 Jun 9 2004 genreprofilerclasstest.py
17125 Aug 25 2004 goombahserverclass.py 25439 Aug 20 2004
goombahserverclasstest.py 1991 Apr 6 2004 heartbeatclass.py 1498
Apr 14 2004 heartbeatclasstest.py 776 Apr 6 2004 listutilities.py
2015 Apr 6 2004 listutilitiestest.py 10666 Jul 27 2004
musicurlclass.py 10843 Jul 27 2004 musicurlclasstest.py 6362 Jul 16
2004 neighborbagclass.py 4886 May 12 2004 neighborclass.py 15170
Sep 17 2004 neighborscannerclass.py 11853 Sep 17 2004
neighborscannerclasstest.py 863 Jul 26 2004
neighborscannerprogressslavemain.py 6191 Sep 16 2004
neighborscannerslaveclass.py 259 Apr 6 2004
neighborscannerslaveclasstest.py 875 Jul 26 2004
neighborscannerslavemain.py 5326 Apr 16 2004
neighborsearcherclass.py 39195 Jul 26 2004
neighborsearcherslaveclass.py 4868 Aug 10 2004 normalize.py 49 Jul
15 2004 normalizefastcompile.sh 278 Jul 15 2004
normalizefastsetup.py 5121 Jul 15 2004 normalizefasttest.py 7182
Jul 15 2004 normalizetest.py 4821 Jun 7 2004 onewayfileclass.py
1218 Apr 14 2004 onewayfileclasstest.py 612 Jun 23 2004
opendialogclass.py 15710 Jul 16 2004 openexclusive.py 5089 Apr 6
2004 openexclusivetest.py 3928 Apr 8 2004 picklepipeclass.py 6662
Apr 14 2004 picklepipeclasstest.py 706 Apr 8 2004
picklepipeclasstestwriter.py 1316 Jul 21 2004
pleasewaitdialogclass.py 24019 Jul 21 2004 plisthandlerclass.py
5930 Jul 21 2004 plisthandlerclasstest.py 3654 Jul 21 2004
processprogressclass.py 3248 Jul 21 2004
processprogressclasstest.py 4145 May 7 2004 progressdialogclass.py
39746 Aug 2 2004 recommenderclass.py 11977 Aug 5 2004
recommenderhandlerclass.py 12262 Jul 14 2004
recommenderhandlerclasstest.py 13947 Jul 26 2004
slaveprocessclass.py 1744 Jun 15 2004 slaveprocessclasstest.py 3539
Jun 4 2004 sortedneighborlistclass.py 4951 Jun 4 2004
sortedneighborlistclasstest.py 50519 Aug 5 2004
tasteprofileclass.py 572 Jul 23 2004
tasteprofileclassrefactorings.txt 4460 Aug 5 2004
tasteprofileclasstest.py 1269 Apr 14 2004 test.py 436 Jun 15 2004
testidlerclass.py 5001 Sep 17 2004 timeutilities.py 7047 Sep 17
2004 timeutilitiestest.py 9366 Apr 5 2004 traceclass.py 1336 Aug 4
2004 transposeexceptions.py 4503 Jul 29 2004 userclass.py 208 Jul
29 2004 userclasstest.py 7902 Apr 5 2004 userdefaultsclass.py 4794
Aug 13 2004 userpathsclass.py 3701 Jul 26 2004
userpathsclasstest.py 12875 Aug 20 2004 utilities.py 13770 Jun 7
2004 utilitiestest.py 6607 Jul 26 2004 versioncheckerclass.py 3728
Sep 17 2004 viewfactoryclass.py 2267 Apr 5 2004 build.xml 2392 Jun
23 2004 web.xml ./cooltunes/webserver/WEB-INF/conf: 37329 Jul 28
2004 TurbineResources.properties ./cooltunes/webserver/WEB-INF/lib:
(empty) ./cooltunes/webserver/database: 2802 Aug 11 2004
MysqlSchema.sql 309 Apr 5 2004 backup-goo.sh
./cooltunes/webserver/java/com/transpose/cooltunes: 3912 Apr 5 2004
BlogList.java 5501 Aug 11 2004 BlogPostList.java 863 Apr 5 2004
CTBlog.java 4515 Apr 5 2004 CTBlogPost.java 2230 Jun 2 2004
ClusteringCandidatesFileWriter.java 1151 Jun 2 2004
ClusteringCandidatesSaver.java 983 Apr 5 2004 GeneralComment.java
7257 Aug 11 2004 GeneralCommentList.java 7811 Apr 5 2004
NearestNeighbor.java 6481 Apr 5 2004 News.java 3223 Apr 5 2004
NewsList.java 8129 Jun 2 2004 PublicProfile.java 17063 Aug 20 2004
RPC2Handler.java 14403 Jun 2 2004 User.java
./cooltunes/webserver/java/com/transpose/cooltunes/serylets: 764
Apr 5 2004 Appinit.java 7013 Apr 5 2004 BlogServlet.java 4432 Apr 5
2004 GeneralCommentServlet.java 671 Apr 5 2004 HelloWorld.java 6371
Apr 5 2004 LoginServlet.java 1277 Apr 5 2004 RPC2.java 6611 Aug 11
2004 UserServlet.java
./cooltunes/webserver/java/com/transpose/libs: (empty)
./cooltunes/webserver/java/com/transpose/util: 321 Apr 5 2004
KeyNotFoundException.java 1026 Apr 5 2004 Mailer.java 1313 Apr 5
2004 XmlRpcFault.java ./cooltunes/webserver/jsps: 2088 Sep 16 2004
about.jsp 1621 Apr 5 2004 blogitem.jsp 953 Apr 5 2004 blogs.jsp
28831 Apr 5 2004 clickwrap.jsp 1345 Sep 13 2004 contact.jsp 1550
Apr 5 2004 createblog.jsp 2600 Apr 5 2004 createuser.jsp 360 Apr 5
2004 dbtest.jsp 1551 Sep 13 2004 discussion.jsp 3402 Sep 13 2004
download.jsp 11304 Apr 5 2004 editblog.jsp 7628 Sep 13 2004 faq.jsp
308 Sep 13 2004 getNumUsers.jsp 2169 Sep 13 2004 index.jsp 1899 Sep
13 2004 login.jsp 520 Apr 5 2004 logout.jsp 1028 Apr 5 2004
mailpassword.jsp 1656 Sep 13 2004 privacy.jsp 2612 Apr 5 2004
releases.jsp 1008 Apr 5 2004 send_verification.jsp 2528 Apr 5 2004
startdiscussion.jsp 1045 Apr 5 2004 style.css 394 Jun 2 2004
test.jsp 293 Jun 2 2004 testclusteringcandidates.jsp 3139 Sep 13
2004 tos.jsp 1078 Apr 5 2004 verify.jsp 171 Apr 5 2004
verify_mailed.jsp 6687 Apr 5 2004 viewblog.jsp 962 Apr 5 2004
viewblogbyuser.jsp 3340 Apr 5 2004 viewdiscussion.jsp 3371 Apr 5
2004 viewforum.jsp ./cooltunes/webserver/jsps/images: (empty)
./cooltunes/webserver/jsps/includes: 0 Apr 5 2004 announcement.jsp
1706 Sep 13 2004 beginbody.jsp 731 Sep 13 2004 endbody.jsp 0 Apr 5
2004 footer.jsp 0 Apr 5 2004 header.jsp 455 Apr 5 2004
jspheader.jsp 2017 Sep 17 2004 build.xml 5524 May 20 2004 web.xml
./songsifter/WEB-INF/conf: 38247 May 20 2004
TurbineResources.properties ./songsifter/WEB-INF/tlds: (empty)
./songsifter/database: 13853 May 20 2004 DemoSchema.sql 5377 May 20
2004 MusicNewsSchema.sql 13306 May 20 2004 MysqlSchema.sql 702 May
20 2004 NewsSchema.sql 1906 May 20 2004 OracleClearData.sql 185 May
20 2004 OracleEMCreator.sql
1975 May 20 2004 OracleFixSequences.sql 3829 May 20 2004
OracleInitValues.sql 3132 May 20 2004 OracleJDBCUser.sql 14427 May
20 2004 OracleSchema.sql 625 May 20 2004 RepairCTXIndexes.sql 5450
May 20 2004 SuggestionSchema.sql 214 May 20 2004 oraclecommands.txt
340 May 20 2004 savepoints.sql 1082 May 20 2004 seq.temp.sql
./songsifter/java/com/transpose: 780 May 20 2004 Makefile 1774 May
20 2004 Makefile.include ./songsifter/java/com/transpose/deed: 6160
May 20 2004 AuctionItem.java 28528 May 20 2004 BackgroundInfo.java
6881 May 20 2004 BestDeedList.java 4896 May 20 2004 Bid.java 2520
May 20 2004 Blog.java 4856 May 20 2004 BlogIDFanID.java 10927 Aug
11 2004 BlogPost.java 976 May 20 2004 ChangedBestDeedList.java 575
May 20 2004 ChangedDeedList.java 5094 May 20 2004 ClickThru.java
10806 May 20 2004 DBTableNames.java 383 May 20 2004
DBTableSelector.java 41674 May 20 2004 Deed.java 12631 May 20 2004
DeedAndChildList.java 8487 May 20 2004 DeedComment.java 922 May 20
2004 DeedIDAndLevel.java 973 May 20 2004 DeedList.java 11195 May 20
2004 DeedListImplementor.java 19138 May 20 2004 DeedRating.java
3576 May 20 2004 DeedRatingTable.java 2501 May 20 2004
DeedTable.java 7493 May 20 2004 Deed_Fan.java 15610 Aug 11 2004
DiscussionComment.java 1419 May 20 2004 FanDeedList.java 4467 May
20 2004 Forum.java 3781 May 20 2004 ForumList.java 3866 May 20 2004
K2Factory.java 5387 May 24 2004 K2User.java 3236 May 20 2004
K2UserList.java 3937 May 20 2004 K2UserOption.java 4497 May 20 2004
K2UserPoints.java 26110 May 20 2004 K2UserValue.java 4248 May 20
2004 K2UserValueTable.java 3880 May 20 2004 MailingList.java 249
May 20 2004 Makefile 4292 May 20 2004
NeedRatingDeedListImplementor.java 264 May 20 2004
NotEnoughPointsException.java 4883 May 20 2004 NotifyEvent.java
10955 May 20 2004 PointsChange.java 5100 May 20 2004
PointsChangeTable.java 249 May 20 2004 Searchable.java 2627 May 20
2004 SearchableDeedListImplementor.java 18586 May 20 2004
Topic.java 8916 May 20 2004 TopicComment.java 2714 May 20 2004
TopicTable.java ./songsifter/java/com/transpose/deed/servlets: 5009
May 20 2004 DeedServlet.java 3046 May 20 2004 EditDeedServlet.java
250 May 20 2004 Makefile 4993 May 20 2004
ModeratorCommentServlet.java 1151 May 20 2004
ServletParameterException.java 3938 May 20 2004
StoreDeedServlet.java ./songsifter/java/com/transpose/deed/test:
580 May 20 2004 testclickthru.jsp 817 May 20 2004 testcounts.jsp
1128 May 20 2004 testdeednumbers.jsp 1465 May 20 2004
testdeedsforfan.jsp 1376 May 20 2004 testhistory.jsp 930 May 20
2004 testlatest.jsp 1996 May 20 2004 testneediest.jsp 611 May 20
2004 testoriginaldeed.jsp 1057 May 20 2004 testresetbest.jsp
./songsifter/java/com/transpose/k2math: 293 May 20 2004
InconsistentDataException.java 26034 May 20 2004 K2MathClass.java
243 May 20 2004 Makefile 345 May 20 2004
NotEnoughDataException.java 203 May 20 2004
PleaseStopException.java 22809 May 20 2004
ProcessBackgroundRatingCutoffs.java 78082 May 20 2004
ProcessDirtyDeedRatings.java 19116 May 20 2004
ReinitializeMath.java ./songsifter/java/com/transpose/libs: (empty)
./songsifter/java/com/transpose/my: 5616 May 20 2004 Affinity.java
2440 May 20 2004 EmailAFriendTopic.java 4849 May 20 2004
Fan_Affinity.java 3725 May 20 2004 Fan_AffinityList.java 6295 May
20 2004 K2MYFactory.java 4595 May 20 2004 Login.java 885 May 20
2004 MYBackgroundInfo.java 4886 May 20 2004 MYBestDeedList.java
1313 May 20 2004 MYChangedBestDeedList.java 1302 May 20 2004
MYChangedDeedList.java 10778 May 20 2004 MYDeed.java 493 May 20
2004 MYDeedList.java 2638 May 20 2004 MYDeedListImplementor.java
1353 May 20 2004 MYDeedRating.java 3481 May 20 2004 MYFan.java 6064
May 20 2004 MYFanList.java 4092 May 20 2004 MYFanOption.java 919
May 20 2004 MYFanValue.java 680 May 20 2004 MYPointsChange.java
2392 May 20 2004 MYScheduledTasks.java 9724 May 20 2004
MYTopic.java 997 May 20 2004 MYTopicComment.java 1172 May 20 2004
MYUser.java 245 May 20 2004 Makefile 537 May 20 2004
ProcessBackgroundMYRatingCutoffs.java 656 May 20 2004
ProcessDirtyMYDeedRatings.java 1066 May 20 2004
ProcessDirtyMYDeedRatingsScheduledTask.java 1055 May 20 2004
ProcessMYBGInfoScheduledTask.java
./songsifter/java/com/transpose/my/servlets: 797 May 20 2004
AppInit.java 18270 May 20 2004 CreateMYDeedServlet.java 8622 May 20
2004 CreateMYPersonServlet.java 5498 May 20 2004
DeedRatingServlet.java 4718 May 20 2004 EditMYDeedServlet.java
28926 May 20 2004 FanServlet.java 7032 May 20 2004
LoginServlet.java 3057 May 20 2004 MYTopicCommentServlet.java 248
May 20 2004 Makefile 4487 May 20 2004 StoreMYDeedServlet.java 4226
May 20 2004 UploadMYPictureServlet.java
./songsifter/java/com/transpose/scheduledjobs: 2707 May 20 2004
JobMinder.java 939 May 20 2004 JobMinderScheduledTask.java 247 May
20 2004 Makefile 326 May 20 2004 PoliteRunnable.java 9403 May 20
2004 ScheduledTask.java 1746 May 20 2004 ScheduledTaskList.java
1774 May 20 2004 TestScheduledTask.java
./songsifter/java/com/transpose/songdeed: 12702 May 20 2004
AlbumAuctionItem.java 7716 May 20 2004 AlbumAuctionItemList.java
1026 May 20 2004 AlbumBid.java 4161 May 20 2004 Announcement.java
3736 May 20 2004 ArtistList.java 2894 Aug 11 2004
ArtistWeeklyEmailMessage.java 4860 May 20 2004 BlogSongs.java 927
May 20 2004 BlogSongsScheduledTask.java 4827 May 20 2004
EMScheduledTasks.java 5051 May 20 2004 EmailAFriend.java 2519 May
20 2004 EmailAFriendTopic.java 25661 Aug 11 2004 Fan.java 6681 May
20 2004 FanList.java 4076 May 20 2004 FanOption.java 11085 May 20
2004 FanSongPointsChangesList.java 4705 May 20 2004 Fan_Genre.java
3905 May 20 2004 Fan_GenreList.java 996 May 20 2004
GeneralComment.java 6984 Aug 11 2004 GeneralCommentList.java 4107
May 20 2004 Genre.java 6712 May 20 2004 K2SongFactory.java 458 May
20 2004 LinkEntry.java 4682 May 20 2004 Login.java 4397 May 20 2004
LoginList.java 251 May 20 2004 Makefile 1616 May 20 2004
NeedRatingDeedList.java 6044 May 20 2004 News.java 3349 May 20 2004
NewsList.java 5396 May 20 2004
ProcessArtistWeeklyPromotionEmail.java 1203 May 20 2004
ProcessArtistWeeklyPromotionEmailScheduledTask.java 1518 May 20
2004 ProcessAuctionResults.java 1049 May 20 2004
ProcessAuctionResultsScheduledTask.java 577 May 20 2004
ProcessBackgroundSongRatingCutoffs.java 4400 May 20 2004
ProcessBids.java 1254 May 20 2004 ProcessBidsDollars.java 1025 May
20 2004 ProcessBidsDollarsScheduledTask.java 1245 May 20 2004
ProcessBidsPoints.java 993 May 20 2004
ProcessBidsPointsScheduledTask.java 708 May 20 2004
ProcessDirtySongDeedRatings.java 1107 May 20 2004
ProcessDirtySongDeedRatingsScheduledTask.java 1098 May 20 2004
ProcessSongBGInfoScheduledTask.java 3318 May 20 2004
ProcessTopScorerContest.java 1103 May 20 2004
ProcessTopScorerContestScheduledTask.java 5514 May 20 2004
PromotedTopic.java 764 May 20 2004 PromotedTopicDollars.java 4121
May 20 2004 PromotedTopicList.java 678 May 20 2004
PromotedTopicListDollars.java 672 May 20 2004
PromotedTopicListPoints.java 759 May 20 2004
PromotedTopicPoints.java 12168 Aug 11 2004 RPC2Handler.java 526 May
20 2004 ReinitializeSongMath.java 903 May 20 2004
SongBackgroundInfo.java 1309 May 20 2004 SongBestDeedList.java 1327
May 20 2004 SongChangedBestDeedList.java 1316 May 20 2004
SongChangedDeedList.java 22561 May 20 2004 SongDeed.java 1005 May
20 2004 SongDeedComment.java 514 May 20 2004 SongDeedList.java 2651
May 20 2004 SongDeedListImplementor.java 602 May 20 2004
SongDeedListSearcher.java 618 May 20 2004 SongDeedNotifyEvent.java
1433 May 20 2004 SongDeedRating.java 3573 May 20 2004
SongDeedValidator.java 3413 May 20 2004 SongDeed_Fan.java 821 May
20 2004 SongFanDeedList.java 933 May 20 2004 SongFanValue.java 8473
May 20 2004 SongLink.java 688 May 20 2004 SongPointsChange.java
4190 May 20 2004 SongSearchBestDeedList.java 11881 May 20 2004
SongTopic.java 7714 May 20 2004 SongTopicBid.java 776 May 20 2004
SongTopicBidDollars.java 915 May 20 2004
SongTopicBidDollarsList.java 4802 May 20 2004 SongTopicBidList.java
774 May 20 2004 SongTopicBidPoints.java 925 May 20 2004
SongTopicBidPointsList.java 1015 May 20 2004 SongTopicComment.java
4888 May 20 2004 Vendor.java
./songsifter/java/com/transpose/songdeed/jobs: 639 May 20 2004
processbids.jsp 497 May 20 2004 processsongratings.jsp
./songsifter/java/com/transpose/songdeed/servlets: 7637 May 20 2004
AlbumBidServlet.java 841 May 20 2004 AppInit.java 4995 May 20 2004
AuctionServlet.java 5335 May 20 2004 DeedRatingServlet.java 5598
May 20 2004 EditSongDeedServlet.java 28595 May 20 2004
FanServlet.java 3695 May 20 2004 GeneralCommentServlet.java 7034
May 20 2004 LoginServlet.java 254 May 20 2004 Makefile 2334 May 20
2004 NewsServlet.java 4153 May 20 2004 PayPalServlet.java 1148 May
20 2004 RPC2.java 3067 May 20 2004 SongDeedCommentServlet.java 925
May 20 2004 SongModeratorCommentServlet.java 629 May 20 2004
SongTopicBidDollarsServlet.java 626 May 20 2004
SongTopicBidPointsServlet.java 5988 May 20 2004
SongTopicBidServlet.java 3100 May 20 2004
SongTopicCommentServlet.java 778 May 20 2004
SpendMyPointsServlet.java 1590 May 20 2004
StoreSongDeedServlet.java 4572 May 20 2004 StressTestServlet.java
./songsifter/java/com/transpose/songdeed/test: 866 May 20 2004
addToFanGenreList.jsp 1102 May 20 2004 reloadblog.jsp 832 May 20
2004 testannouncement.jsp 634 May 20 2004 testartistwebsite.jsp 469
May 20 2004 testblog.jsp 847 May 20 2004 testdeedfanlist.jsp 2043
May 20 2004 testerror.jsp 2134 May 20 2004 testfanoption.jsp 979
May 20 2004 testfanpoints.jsp 562 May 20 2004 testgenres.jsp 829
May 20 2004 testgetlink.jsp
3703 May 20 2004 testpoints.jsp 792 May 20 2004
testpromotedtopiclist.jsp 1636 May 20 2004 testsearch.jsp 696 May
20 2004 testshowsongdeed.jsp 3179 May 20 2004 testsongdeed.jsp 724
May 20 2004 testsongdeed_fan.jsp 911 May 20 2004
testsongdeedhistoryvector.jsp 1534 May 20 2004 testsongdeedlist.jsp
4594 May 20 2004 testsongdeedrating.jsp 681 May 20 2004
testsongdeedvalue.jsp 755 May 20 2004 testsongfanvalue.jsp 997 May
20 2004 testsongtopicbid.jsp 1105 May 20 2004
testsongtopicbidlist.jsp 1131 May 20 2004
testsongtopicbidpointslist.jsp 1827 May 20 2004
testsongtopiccomment.jsp 902 May 20 2004
testsongtopiccommentdate.jsp 1366 May 20 2004
testsongtopiccommentlist.jsp 1063 May 20 2004
testsongtopicexists.jsp ./songsifter/java/com/transpose/tags: 803
Jul 28 2004 DisplayAIM.java 7129 Jul 28 2004
DisplayDeedHistory.java 1763 Jul 28 2004
DisplayGenreCheckboxList.java 1349 Jul 28 2004
DisplayGenreCheckboxListLoggedIn.java 994 Jul 28 2004
DisplayGenreDropDown.java 806 Jul 28 2004 DisplayICQ.java 1633 Jul
28 2004 DisplayLatestDetailedNews.java 1179 Jul 28 2004
DisplayLatestNews.java 3351 Jul 28 2004 DisplayListNavigation.java
3912 Jul 28 2004 DisplayPlainMusicLinks.java 1217 Jul 28 2004
DisplayPresetGenreDropDown.java 375 Jul 28 2004
DisplaySongBestDeedList.java 421 Jul 28 2004
DisplaySongChangedBestDeedList.java 413 Jul 28 2004
DisplaySongChangedDeedList.java 16634 Jul 28 2004
DisplaySongDeedList.java 893 Jul 28 2004
DisplaySongFanDeedList.java 3371 Jul 28 2004 DisplaySongLinks.java
907 Jul 28 2004 DisplaySongNeedyDeedList.java 1385 Jul 28 2004
DisplaySongSearchBestDeedList.java 2945 Jul 28 2004
DisplayTopScorers.java 2857 Jul 28 2004 DisplayTopScorersToday.java
1366 Jul 28 2004 DisplayTopScouts.java 1370 Jul 28 2004
DisplayTopWriters.java 979 Jul 28 2004 FairtunesSearchURL.java 239
Jul 28 2004 Makefile 921 Jul 28 2004 Picture.java 1319 Jul 28 2004
VendorList.java 1601 Jul 28 2004 VendorSearchURL.java
./songsifter/java/com/transpose/tags/test: 473 Jul 28 2004
testtopscorers.jsp ./songsifter/java/com/transpose/util: 1531 May
20 2004 Assert.java 5363 May 20 2004 BreadCrumbs.java 1302 May 20
2004 CookieUtils.java 722 May 20 2004 DBConfig.java 3000 May 20
2004 DBConnectionHelper.java 1624 May 20 2004 DBQueryHelper.java
2009 May 20 2004 DBUpdateHelper.java 2393 May 20 2004
DateUtils.java 7010 May 20 2004 DocumentObject.java 2134 May 20
2004 Dumper.java 1267 May 20 2004 DynamicPagedList.java 11297 May
20 2004 ElementObject.java 4450 May 20 2004 ErrorNotifier.java 4059
May 20 2004 HashUtilities.java 2465 May 20 2004 ID.java 321 May 20
2004 KeyNotFoundException.java 5937 Sep 8 2004
KeyedStoreRecord.java 555 May 20 2004 LoggedException.java 1026 May
20 2004 Mailer.java 247 May 20 2004 Makefile 13714 May 20 2004
Normalize.java 1448 May 20 2004 PagedList.java 8154 May 20 2004
PreparedStatementHelper.java 2519 May 20 2004 RSSDocument.java 1017
May 20 2004 RSSEnclosure.java 1844 May 20 2004 RSSItem.java 2237
May 20 2004 RadioBlogger.java 1069 May 20 2004 RandomString.java
5422 May 20 2004 Rating.java 6068 May 20 2004 ResultSetHelper.java
982 May 20 2004 SQLFormat.java 458 May 20 2004 Singleton.java 3166
May 20 2004 SingletonStoreRecord.java 3672 May 20 2004
SongHash.java 23556 Sep 8 2004 StoreRecord.java 656 May 20 2004
StringDumper.java 5193 May 20 2004 StringFormat.java 900 May 20
2004 TestURL.java 3474 May 20 2004 TransactionConnection.java 621
May 20 2004 WaitThread.java 657 May 20 2004
XMLParsingException.java 1671 May 20 2004 XercesErrorHandler.java
6628 May 20 2004 XmlWriter.java
./songsifter/java/com/transpose/util/servlets: 248 May 20 2004
Makefile ./songsifter/jsps: 4215 May 20 2004 about.jsp 4976 May 20
2004 aboutartists.jsp 2419 May 20 2004 aboutauctions.jsp 7213 May
20 2004 aboutcriteria.jsp 2982 May 20 2004 abouthosting.jsp 5330
May 20 2004 aboutnewmusic.jsp 3313 May 20 2004 aboutpoints.jsp 3747
May 20 2004 aboutpredict.jsp 2874 May 20 2004 aboutrecommend.jsp
4670 May 20 2004 aboutreviews.jsp 3798 May 20 2004 aboutsponsor.jsp
4074 May 20 2004 aboutthecompetition.jsp 930 May 20 2004
addtomailinglist.jsp 1082 May 20 2004 admin.jsp 2407 May 20 2004
allbuckssponsors.jsp 1515 May 20 2004 allpointssponsors.jsp 2407
May 20 2004 allsponsors.jsp 4960 May 20 2004
artistalreadyloggedin.jsp 2671 May 20 2004 artistlist.jsp 4073 May
20 2004 audiohelp.jsp 3893 May 20 2004 badge.jsp 448 May 20 2004
badge_bestrecs.jsp 3196 May 20 2004 badgedata_bestrecs.jsp 4303 May
20 2004 badges.jsp 895 May 20 2004 badgestyle.css 8285 May 20 2004
best.jsp 7323 May 20 2004 changed.jsp 4193 May 20 2004
changedbest.jsp 1717 May 20 2004 changegenres.jsp 9458 May 20 2004
confirmalbumbid.jsp 1947 Aug 6 2004 contact.jsp 1704 May 20 2004
copyright.jsp 8752 May 20 2004 create.jsp 6597 May 20 2004
createaccount.jsp 6632 May 20 2004 createartistaccount.jsp 10453
May 20 2004 createartistrec.jsp 3887 May 20 2004
createartistrecthanks.jsp 2765 May 20 2004 createbid.jsp 2294 May
20 2004 deedstats.jsp 8086 May 20 2004 discussion.jsp 8717 May 20
2004 edit.jsp 8738 May 20 2004 editartistrec.jsp 3741 May 20 2004
emailafriend.jsp 2375 May 20 2004 error.jsp 1309 May 20 2004
fanheader.jsp 3402 May 20 2004 fanlist.jsp 25449 May 20 2004
faq.jsp 2028 May 20 2004 friends.jsp 5880 May 20 2004
gettingstarted.jsp 3311 May 20 2004 help.jsp 501 May 20 2004
help_artistweeklyemail.jsp 543 May 20 2004 help_asterisks.jsp 416
May 20 2004 help_beta.jsp 416 May 20 2004 help_mailinglist.jsp 647
May 20 2004 help_musiclist.jsp 491 May 20 2004 help_mypoints.jsp
621 May 20 2004 help_myprivate.jsp 537 May 20 2004
help_mypublic.jsp 486 May 20 2004 help_mysite.jsp 576 May 20 2004
help_sponsoreddollars.jsp 592 May 20 2004 help_sponsoredpoints.jsp
535 May 20 2004 help_topscorers.jsp 670 May 20 2004
help_toratelist.jsp 317 May 20 2004 helppopupend.jsp 585 May 20
2004 helppopupheader.jsp 519 May 20 2004 helppopupstart.jsp 4622
May 20 2004 index.jsp 1564 May 20 2004 l.jsp 3116 May 20 2004
lastloginlist.jsp 2141 May 20 2004 lastmusiccomments.jsp 2175 May
20 2004 lastratingnotes.jsp 2186 May 20 2004
lastrecommendationcomments.jsp 6545 May 20 2004 login.jsp 304 May
20 2004 logout.jsp 3190 May 20 2004 mailpassword.jsp 6127 May 20
2004 memberprofile.jsp 7522 May 20 2004 music.jsp 9332 May 20 2004
musiccomments.jsp 9065 May 20 2004 musicdiscussion.jsp 5787 May 20
2004 mypoints.jsp 17942 May 20 2004 mysettings.jsp 12072 May 20
2004 needrating.jsp 2103 May 20 2004 newartistintro.jsp 4002 May 20
2004 newmusiclinks.jsp 4355 May 20 2004 newsletter-1-1.jsp 6165 May
20 2004 newsletter-1-2.jsp 116 May 20 2004 openLetter.jsp 552 May
20 2004 paypalfail.jsp 546 May 20 2004 paypalsuccess.jsp 23982 May
20 2004 positiverecommendation.jsp 7270 May 20 2004 preview.jsp
6849 May 20 2004 previewartistrec.jsp 2031 May 20 2004 privacy.jsp
3528 May 20 2004 quickstart.jsp 9052 May 20 2004 ratingnotes.jsp
8962 May 20 2004 recommendationcomments.jsp 88 May 20 2004
robots.txt 3058 May 20 2004 rssfeed.jsp 2727 May 20 2004
rssfeedsexplained.jsp 3598 May 20 2004 rulesforgoodreviews.jsp 7523
May 20 2004 searchresults.jsp 886 May 20 2004 showbadge.jsp 428 May
20 2004 siteoffline.jsp 9737 May 20 2004 spendmypoints.jsp 4335 May
20 2004 sponsorasong.jsp 3682 May 20 2004 sponsoredmusicbucks.jsp
3764 May 20 2004 sponsoredmusicpoints.jsp 4610 May 20 2004
sponsorwithbucks.jsp 6706 May 20 2004 sponsorwithpoints.jsp 2650
May 20 2004 startdiscussion.jsp 7417 May 20 2004 stats.jsp 789 May
20 2004 stresstesting.jsp 5667 May 20 2004 style.css 3201 May 20
2004 template.jsp 3280 May 20 2004 testvalidity.jsp 3537 May 20
2004 topmonthlyscorerslist.jsp 814 May 20 2004 topreviewwriters.jsp
3140 May 20 2004 topscorerslist.jsp 804 May 20 2004 topscouts.jsp
4260 May 20 2004 tos.jsp 5865 May 20 2004 updateemailsettings.jsp
8251 May 20 2004 updatememberprofile.jsp 5404 May 20 2004
updatepublicprofile.jsp 5836 May 20 2004 updatesitesettings.jsp 906
May 20 2004 values.jsp 2259 May 20 2004 verify.jsp 1408 May 20 2004
verify_failed.jsp 738 May 20 2004 verify_mailed.jsp 27188 May 20
2004 view.jsp 7399 May 20 2004 viewalbumauctionitem.jsp 3393 May 20
2004 viewdiscussion.jsp 3562 May 20 2004 viewforum.jsp 254 May 20
2004 viewreview.jsp 858 May 20 2004 waitforverify.jsp 65 May 20
2004 weblog.jsp 2255 May 20 2004 whyrate.jsp
./songsifter/jsps/includes: 1107 May 20 2004 announcement.jsp 640
May 20 2004 autologin.jsp 440 May 20 2004 beginbody.jsp 229 May 20
2004 endbody.jsp 2523 May 20 2004 footer.jsp 9388 May 20 2004
header.jsp 3646 May 20 2004 jspheader.jsp 494 Sep 8 2004 notice.jsp
785 May 20 2004 retrievepoints.jsp 670 May 20 2004 setuppaging.jsp
2151 May 20 2004 sidebarauctions.jsp 442 May 20 2004
sidebarbadge.jsp 974 May 20 2004 sidebardiscuss.jsp 2228 May 20
2004 sidebarmailinglist.jsp 1945 May 20 2004 sidebarmypoints.jsp
3424 May 20 2004 sidebarsponsoredmusic.jsp 2458 May 20 2004
sidebartopdailyscorers.jsp 926 May 20 2004
sidebartopscorers.jsp
845 May 20 2004 songdeedlistheader.jsp
TECHNICAL FIELD
[0004] The present invention is in the fields of collaborative
filtering and online community, typically as implemented on
networks of communicating computers.
BACKGROUND ART
[0005] Collaborative filtering systems are well known, as are
online community systems. Examples of the former include
Amazon.com's recommendation technology and other similar systems
such as eMusic.com's. Examples of the latter include Google
Groups.
[0006] However, none of the existing solutions effectively
leverages the fact that users of online recommendations systems and
online community systems typically own their own computers, and
have the opportunity to make the central processing units of those
computers available for making such systems more useful and
enjoyable.
[0007] In particular, the task of matching people with extremely
similar tastes and interests becomes very computationally difficult
as the number of people increases and as the complexity of the
similarity measure increases. With hundreds of thousands or even
millions of people such as are typically enrolled in major online
services, limitations of server hardware resources constrain the
system's ability to find the best matches between people based on
taste and interest.
[0008] To the degree that such matches are made with real accuracy,
"neighborhoods" of individuals with extremely similar interests may
be formed that can be used for purposes of recommendation and
community.
[0009] What is needed, then, is an effective way of leveraging the
computers owned by end-users of a community and recommendation
system for the purpose massively-distributed similarity
searching.
SUMMARY OF THE INVENTION
[0010] The present invention puts the computer used by a particular
end-user (the `client computer` or `client machine`) to work in
finding his or her best matches, thus offloading that computational
load from the server. (In some variants, some users' computers may
do that work for a manageable number of other users; for purposes
of example this summary will not discuss those details.)
[0011] To enable the computations to occur in the client machines,
the necessary data needs to be transported there. This data
consists, at least in part, of `profiles` of various users. Various
embodiments do this in different ways, the common denominator being
that profiles that are relatively likely to be matches to the user
for whom neighbors are being sought arrive first.
[0012] Then the client computer conducts a substantially (or
completely) exhaustive search of that available data for the very
best matches.
[0013] Typically at least part of the profile data performs a dual
purpose. First it is used for similarity calculations. Second, it
is used for display purposes, so that a user can view taste
information pertaining to his neighbors. For instance, in a typical
music application, this will include song title and artist
information for songs in the neighbors' collections.
[0014] This disclosure will make use of a detailed listing of key
aspects, followed by a glossary containing definitions for terms
used therein.
[0015] ASPECT 1. A networked computer system for supplying
recommendations and taste-based community to a target user,
comprising:
[0016] networked means for providing representations of nearest
neighbor candidate taste profiles and associated user identifiers
in an order such that said nearest neighbor candidate taste
profiles tend to be at least as similar to a taste profile of the
target user according to a predetermined similarity metric as are
subsequently retrieved ones of said nearest neighbor candidate
taste profiles,
[0017] means to receive said representations of nearest neighbor
candidate taste profiles and associated user identifiers on at
least one neighbor-finding user node,
[0018] said neighbor-finding user nodes each having at least one
similarity metric calculator calculating said predetermined
similarity metric,
[0019] at least one selector residing on at least one of said
neighbor-finding user nodes using the output of said at least one
similarity metric calculator for building a list representing the
nearest-neighbor users,
[0020] said list representing said nearest-neighbor users providing
access to associated ones of said candidate profiles,
[0021] a nearest-neighbor based recommender which uses said
associated ones of said candidate profiles to recommend items,
[0022] a display for viewing identifiers of recommended items,
[0023] a display for viewing identifiers of a plurality of nearest
neighbor users,
[0024] means to select at least one of said nearest neighbor users
from said display of identifiers of a plurality of nearest neighbor
users,
[0025] a display of information relating to at least one of the
items in said nearest neighbor user's collection,
[0026] whereby massively distributed processing is harnessed in a
bandwidth-conserving way for finding the best neighbors out of the
entire population of users, and the same neighborhood is leveraged
to provide recommendations as well as highly focused taste-based
community for sharing the enjoyment of items including recommended
items
[0027] ASPECT 2: The networked computer system of ASPECT 1, further
including means to facilitate communication with at least said
nearest neighbor users where the type of communication comprises at
least one selected from the group consisting of online chat, email,
online discussion boards, voice, and video.
[0028] ASPECT 3: A networked computer system for supplying
recommendations and taste-based community to a target user,
comprising
[0029] an ordered plurality of nearest neighbor candidate taste
profiles and associated user identifiers such that said nearest
neighbor candidate taste profiles tend to be at least as similar to
a taste profile of the target user according to a predetermined
similarity metric as are subsequently positioned ones of said
nearest neighbor candidate taste profiles,
[0030] networked means to receive said nearest neighbor candidate
taste profiles and associated user identifiers on at least one
neighbor-finding user node,
[0031] said neighbor-finding user nodes each having at least one
similarity metric calculator calculating said predetermined
similarity metric,
[0032] at least one selector residing on at least one of said
neighbor-finding user nodes using the output of said at least one
similarity metric calculator for building a list representing the
nearest-neighbor users,
[0033] said list representing said nearest-neighbor users providing
access to associated ones of said candidate profiles,
[0034] a nearest-neighbor based recommender which uses said
associated ones of said a nearest-neighbor based recommender which
uses said associated ones of said candidate profiles to recommend
items,
[0035] a display for viewing identifiers of recommended items,
[0036] a display for viewing identifiers of a plurality of nearest
neighbor users,
[0037] means to select at least one of said nearest neighbor users
from said display of identifiers of a plurality of nearest neighbor
users,
[0038] a display of information relating to at least one of the
items in said nearest neighbor user's collection,
[0039] whereby massively distributed processing is harnessed in a
bandwidth-conserving way for finding the best neighbors out of the
entire population of users, and the same neighborhood is leveraged
to provide recommendations as well as highly focused taste-based
community for sharing the enjoyment of items including recommended
items
[0040] ASPECT 4: The networked computer system ASPECT 1, further
including a single downloadable file that contains software that
executes all necessary non-server computer instructions.
GLOSSARY
[0041] REPRESENTATION: In the above discussion of "aspects,"
representations may be the user profiles themselves (including the
taste profiles), or just the taste profiles (which should include
an identifier of the user)--or they may be user ID's of the users,
or URL's enabling the data to be located on the network, or any
other data that allows taste profiles and associated user ID's to
be accessed. These are all functionally equivalent from the
standpoint of the invention. TASTE PROFILE: This term refers to
data representing an individual's tastes or interests. It can take
many forms. It may be the XML file generated by Apple's iTunes
application which contains a list of music files in the user's
collection as well as how many times he has played each one, and
other related information. This is a fairly complete profile,
having the disadvantage that it tends to consume a fairly large
number of bytes that thus take significant bandwidth to
download.
[0042] Other profile types include simple lists of song identifiers
or album or artist identifiers, or various combinations thereof. In
non-music domains, other examples include book ISBN's, or author
names, or combinations thereof; or weblog URL's, or weblog posting
identifiers, or combinations thereof; of any of a multitude of
other represenations of a user's tastes and/or interests.
[0043] Just as different profile types may contain various
different types of data, there are many formats that can be used
for representing such data to be processed by a computer. XML is
one, but such specifications as CORBA and many others provide ways
that data objects can be represented and transported across a
network, and in general such formats as vectors or other binary or
text-based formats can be used.
[0044] A taste profile is data that represents a user's tastes
and/or interests. The format and contents are particular to
particular embodiments, and it must not be construed that the
present invention is limited in scope to particular contents or
formats as long as the data comprises a user's tastes and/or
interests or some useful summary thereof.
[0045] Further, it should be noted that a user may have a plurality
of taste profiles. For instance, a user may have one type of music
he likes to listen to while studying, and another type he likes to
listen to while dancing. Preferred embodiments of the invention
allow the user to choose different taste profiles--and
correspondingly different nearest neighbors and
recommendations--according to mood.
[0046] Still further, note that taste profiles may be either
manually or passively generated. For instance the iTunes
application captures user activity in the course of playing music,
and stores it to its associated XML file. The user does not have to
make any separate effort to cause a taste profile to be generated
based upon that data. On the other hand, taste profiles can be
manually generated by manually supplying ratings to items such as
songs, movies, or artists. A playlist--a list of songs a user likes
to play together, and which has usually been generated
manually--can be considered in some embodiments to be a taste
profile. Some embodiments use taste profiles that incorporate a
combination of passively and actively collected data. For instance,
a profile may include manually-generated ratings of songs, as well
as the number of times each song has been played.
[0047] Finally, note that taste profiles do not necessarily include
data directly entered by the user; they can instead be a
computer-derived representation. For instance, in embodiments which
associate information such as genre or tempo for songs, software
developers of ordinary skills will be able to see how to summarize
data for songs the user has in his collection to create a profile
showing which genres or tempos the user likes most; that
information may then comprise the user's taste profile. Or, in
certain embodiments with numeric values for attributes, the log of
the values may be used.
TARGET USER: The aspect discussion describes the invention in a way
that focuses on serving a particular user, who we call the "target
user." There are a plurality of users who could be considered to be
target users, but for descriptive purposes we focus on one such
user. USER PROFILE: A user profile contains information related to
the individual such as his name, contact information, and
biographical text. It also contains his taste profile. An
embodiment may make all, some, or none of this information publicly
available. SIMILARITY METRIC: Degrees of similarity are computed
according to a similarity metric, which is not necessarily a
"metric" in the formal sense of a "metric space" as that term is
used in mathematical literature (for instance
http://en.wikipedia.org/wiki/Metric_space). A very great variety of
similarity metrics are available. There is necessarily a
correspondence between the nature of the similarity metric and the
taste profile, because similarity metrics often require particular
types of data.
[0048] For instance, if ratings data is present where numerical
values are given such as on a scale from 1 to 7 where 1 is poor and
7 is excellent, such simple methods can be used as computing
average difference between the ratings of the items which have
ratings in both taste profiles. Other techniques include computing
a Euclidean distance, Mahalanobis distance, cosine similarity, or
Pearson's r correlation using that data [13, 15]. Another approach
is given in [16], beginning column 20, line 59. Any other
computation that results in a metric that tends to be indicative of
similarities of taste between the two users can be used.
[0049] In many embodiments data is massaged to make it more
appropriate use with certain popular similarity metrics. For
instance, in a music application when song play counts are included
in the taste profile, the songs may be ranked in order of frequency
of play; songs in the top seventh have an "implied rating" of 7,
songs in the next seventh have an implied rating of 6, etc. This
data can then be used with similarity metrics such as those
mentioned above.
[0050] Note that some similarity metrics, such as Pearson's r,
enable the computation of levels of probabilistic certainty, or
p-values, with respect to a null hypothesis. In many cases, such as
r, it is possible to state a null hypothesis that roughly
corresponds to the concept "the two users have no particular
tendency to agree." This enables the system to take into account
the fact that some pairs of users have more data to base the metric
on then others, and thus more reason to have confidence. This is a
significant advantage over many of the simpler techniques. However,
this approach nevertheless has a drawback. As an example consider
two users with a very large number of items in common which they
have each rated, where a p-value derived from r is used as the
metric. Suppose further that on average, there is a slight tendency
to agree rather than disagree. Then, simply due to the large number
of items with ratings in common, the p-value may be extremely
indicative of rejection of the null hypothesis, even though on
average, there isn't a very unusual amount of agreement between
ratings. In practical use with a large number of users, where not
too many nearest neighbors need to be found, this effect is
normally not a major problem, because there will also be users who
do have a lot of agreement and who also have a high number of rated
items in common, and such pairings will result in even greater
extremities of p-values. In such cases, there can be a lot of
confidence that the similarity metric is finding users who are
actually very similar in taste--even though there may be other
pairings, with even more similarity, that are left behind due to
not having as much data for comparison.
[0051] The immediately preceding paragraphs focus on situations
where degrees of agreement can be discerned for each item. Another
type of profile involves presence/absence data--where all that is
known about each item is whether a user is associated with it or
not--for instance whether a user has a particular song in his
collection or not. In such cases, such calculations as the
well-known Jaccard's Index, Sorensen's Quotient of Similarity, or
Mountford's Index of Similarity can be useful.
[0052] Some embodiments combine different similarity metrics. For
instance r can be used to compute a degree of similarity in ratings
of items that are in common between two users, and Jaccard's Index
to compute the degree of similarity implied by the numbers of items
that are and are not in common between the users. An average or
geometric mean (weighted or not) may be used to combine the metrics
into one that incorporates both kinds of information; other
techniques such as p-value combining with respect to a null
hopothesis ([16]) can be used as well, by converting the metrics
into p-values.
[0053] Source code described in the file tasteprofileclass.py in
Appendix 4 and included in the computer program listing appendix
submitted on CD pursuant to 37 C.F.R. 1.96 takes a different
approach for computing similarity based on iTunes' XML file.
Consider a "shared song" to be a song that is in the collection of
both users. This method calculates an approximate probability that
the next shared song to come into existence will be the next song
played. That is, if user A takes a recommendation from B's
collection, it will be a song that A doesn't have yet. When he has
it, it will be another shared song. What is the probability that it
will be the next song played, once it is in A's collection? This is
a particularly appropriate similarity measure, because it measures
similarity of tastes in a way that directly relates to a key
purpose of finding nearest neighbors: making recommendations that
the user will want to play frequently. Details of the algorithm
appear in the source code. That algorithm is the currently
preferred similarity metric.
[0054] The only requirement of the similarity metric is that, for a
significant portion of pairs users which includes those who tend to
be the most similar in taste, the following applies: if the
calculated similarity of two taste profiles A and B is greater than
the calculated similarity of two taste profiles A and C, then it is
likelier than not that users A and B are actually more similar in
relevant tastes than are users A and C. This likelihood will be
greater for similarity metrics that will be associated with the
highest-performing embodiments of the invention. For instance,
simply using the average distance between ratings may be acceptable
for some applications, but using Euclidean distance is better than
a simple average.
[0055] There are many ways to calculate similarity. Other than the
requirement above, the invention has no dependence on the
particular similarity metric that may be chosen by a particular
embodiment. The invention must not be construed to be limited to a
particular similarity metric or type of similarity metric; the ones
listed here are for reasons of example only. Similarity metrics are
interchangeable for purposes of the invention.
MEANS FOR FACILITATING RETRIEVAL OF REPRESENTATIONS: There are a
variety of ways to provide the functionality needed. It must be
stressed that all provide identical or equivalent functionality for
the purposes of the invention. While there are several basic
structures available, there are many variants for each that are
only insubstantially different and should not be construed as
different in a way that would make them fall outside the scope of
the invention.
[0056] What is needed is a means for facilitating retrieval of
representations of nearest neighbor candidate taste profiles and
associated user identifiers in an order such that said nearest
neighbor candidate taste profiles tend to be at least as similar to
a taste profile of the target user according to a predetermined
similarity metric as are subsequently retrieved ones of said
nearest neighbor candidate taste profiles.
[0057] The representations mentioned in the previous paragraph may
be the user profiles themselves (including the taste profiles), or
just the taste profiles (which should include an identifier of the
user)--or they may be user ID's of the users, or URL's enabling the
data to be located on the network, or any other data that allows
taste profiles and associated user ID's to be accessed. These are
all functionally equivalent from the standpoint of the
invention.
[0058] It is important to note that the means for facilitating this
retrieval does not need to make use of the predetermined similarity
metric or a calculator that can calculate it. In particular, it
isn't required that the retrieval of representations is exactly in
the same order that would be given by the similarity metric.
[0059] One implication of this is that even if the similarity
metric is not a metric in the sense of a metric space, a metric
space-based metric can be used in the means for facilitating this
retrieval. This makes available a large number of algorithms in the
literature for facilitating the retrieval.
[0060] In preferred embodiments the data used in facilitating this
retrieval is a subset of the data used in the similarity metric, or
a summary derived from that data, or a combination of the two, in
order to lower computational costs.
1) Pre-Existing Data Structures
[0061] Data structures may be created that provide the foundation
for retrieval in the necessary order or sequence. For instance,
clustering may be done using a variety of methods. See, for
example, [1] and [2] which apply to "metric spaces," that is, a
structure involving a distance function where the function used to
compute the distance between any two objects satisfies the
positivity, symmetry, and triangle inequality postulates. Such a
distance function can be a similarity metric; examples include
Euclidean distance.
[0062] See also [3] which works on large binary data sets where
data points have high dimensionality and most of their coordinates
are zero. For instance this can be used to cluster based upon
attributes consisting of indicators of whether or not a user has a
particular song in his collection. See also [4].
[0063] Appendix 4 describes source code (genrerankhandler.py),
which appears on the computer program listing appendix, and which
contains an algorithm which uses genre data (genrerankhandler.py),
but a practitioner of ordinary skill in the art will see how to
modify it for use with other kinds of data which is of limited
dimensionality.
[0064] For a given clustering scheme, practitioners of ordinary
skill in the art will know how to compare a particular taste
profile to a particular cluster of taste profiles, and thus
determine an affinity between each cluster and the taste
profile.
[0065] Then, the cluster with the most computed affinity to the
given taste profile is first in the retrieval order, the cluster
with the next most computed affinity is then returned next, etc. Of
course, there can be some degree of difference from this strict
order without violating the spirit of the invention or moving
outside its scope. When we discuss retrieving a cluster, we mean
either a set of representations of nearest neighbor candidate user
profiles, or a representation of such representations. For instance
such a representation can be the name or Internet address of a file
containing the representations of candidates.
[0066] Another approach which uses clustering is given in [5].
[0067] Clusters are not the only kind of structure that can be
used. See, for example, [6] and [4]. Practitioners of ordinary
skill will see how to use such structures for retrieving in an
order consistent with the needs of the invention. Many such
structures with different details of implementation, but these
details are not substantial differences for the purposes of the
invention. It is not possible to list all possible combinations of
such details, and it must not be construed that one can move
outside of the scope of the invention merely by finding such
variations on the structures listed here, which it cannot be
stressed enough are listed for reasons of example only.
[0068] The source code in Appendix A provides the exemplary key
aspects of one particular method for causing the representations to
be retrieved in an order consistent with the needs of the
invention. See the explanatory text in the section for
clusterfitterclass.py.
[0069] Of course preferred embodiments update or replace these
structures over time as taste profiles associated with users
change, and users are added to or removed from the database
associated with the embodiment.
[0070] Note further that the data structure may be built and stored
on a central server, on machines owned by end-users of the
invention which communicate their results directly to a server
and/or to other end-user machines via peer-to-peer means, or on a
combination. It must not be construed that a system falls outside
of the scope of the invention merely because the necessary
computational and storage resources for the foundation for
retrieval are provided at one location or set of locations rather
than another, or one type of network node rather than another.
[0071] As one example of a combined approach, consider [7]. That
paper provides an algorithm to do clustering based on nearest
neighbors. It can be leveraged to produce a combined approach as
follows.
[0072] Use a peer-to-peer system such as the Gnutella protocol or
any other protocol that enables one to search for a file. Each
end-user machine is a node in such a network, also known as a
"cloud."
[0073] Each end-user machine then conducts a search for each file,
or a substantial subset, of files that are already in that
machine's collection, using the words in the name of each fie (or a
substantial subset of them). A "hit" occurs when the protocol
returns an identifier of a node that has a file with matching words
in its name.
[0074] Some searches will get more "hits" than others.
[0075] For purposes of the algorithm in [7], "nearest neighbors"
will have a different definition than the one involving the
predetermined similarity metric of the present invention. It
involves a couple of components.
[0076] The first component is "hit-nearness." Suppose a query
returns only 1 hit. That means that the node identified by that hit
is considered to be in the first tier of hit-nearness. If it
returns 2 hits, each of the nodes are considered to be in the
second tier of hit-nearness. And so on. The tiers are ranked, and
the ranks are divided by the number of tiers. If T is the number of
tiers, the best hit-nearness is 1/T, the next best is 2/T, and the
worst is T/T (1).
[0077] The next component is "quantity-nearness". We count the
number of times a particular node's identifier is retrieved in the
process of searching for files. We create tiers based on those
numbers using the same tiered approach as for hit-nearness, and
again resulting in a number between 0 and 1 where the worst
node--the node with the smallest number of hits--has a
quantity-nearness, Q, of 1.
[0078] Then the distance of a node to the node doing the search is
the square root of T*Q. So the ordering of each node's the
neighbors for the algorithm in [7] is laid out that way.
[0079] The work of finding neighbors for [7] is thus carried out on
the end-user machines. Then, that nearest neighbor information is
uploaded to the server from each node, and the algorithm in [7] is
carried out there.
[0080] For instance, the algorithm could include Gnutella protocol
code, and use the procedure described above to cluster similar
taste profiles together, where similarity is determined by having
more neighbors in common (rather than by our predetermined
similarity metric).
[0081] Then to determine the order in which clusters should be
downloaded to a particular user's node, the one that contains the
greatest number of his neighbors should be downloaded first, then
the one that has the next greatest number of his neighbors,
etc.
2) Dynamic Searches for Neighbor Candidates
[0082] Instead of, or in combination with, pre-existing data
structures such as described above, many embodiments use dynamic
searches.
[0083] Probably the simplest example of this is a server-based
system with a table of attributes culled from the taste profiles,
one row per user. In one embodiment these attributes are bits
representing the presence or absence of particular genres. So, if
there are 100 defined genres, each row has 100 bits.
[0084] Then to determine the order in which taste profiles should
be downloaded, the server simply checks each row and counts the
proportion of matching genres to total genres in the other user's
taste profile. The representations of taste profiles with the
highest proportions are retrieved first. The table could be a
RAM-based bitmap, a database such as based upon SQL, or any other
convenient configuration. Of course the data used wouldn't have to
be genres. It could be a selection of artists or songs or albums,
or in non-music domains, book titles, web logs, paintings, news
articles, school subjects, course numbers, etc.
[0085] In another set of embodiments, there is virtually no
server-based processing at all; the only server processing is to
supply network addresses for a set of seed nodes that may be online
at the time, which may in fact be included with the download of the
software that executes the computer steps involved in the
invention.
[0086] In these embodiments, a peer-to-peer protocol such as
Gnutella's is used to conduct searches for files, as described
above in this text. Note that if a pre-existing, popular protocol
such as Gnutella's is used, it should be modified so that a node
can respond to a request for a complete taste profile; if that does
not include a list of all (or a substantial subset of) items on the
node's machine, then nodes should also be able to respond to a
request for such items.
[0087] As described elsewhere in this specification, a node (we
will refer to it as the "target node") initiates searches for files
it has in its collection. Nodes that are the subject of hits are
candidate nearest neighbors. Nodes that have more files matching
the target node's files than others are statistically more likely
to be hit before nodes with a smaller number of files. The
representation that comes along with the hit is then used for the
taste profile and if necessary the list of files. So, that
satisfies the requirement of the means for facilitating retrieval
in the desired order. No other server activity is required.
[0088] Note that to increase the performance over protocols such as
Gnutella that are popular at the current time, currently preferred
embodiments use the peer-to-peer method described in [12]. Also, at
the time that user machines connect for a new session in the
peer-to-peer network, they should connect to randomly chosen seed
nodes in order to increase the randomness of results obtained from
searches.
[0089] It must not be construed that the scope of the present
invention is limited to the particular techniques listed here.
3) Note on Retrieval Techniques
[0090] Whether the means for facilitating retrieval is based upon a
pre-existing data structure or whether dynamic computations are
done, there is still the question of actually delivering the
representations of nearest neighbor candidate profiles, and if
separate, the profiles themselves.
[0091] In some embodiments these come directly from the server. In
others such as peer-to-peer techniques like those described above,
they may be the result of direct communication with the machine
owned by the user whose profile is required.
[0092] In some embodiments, caching solutions such as BitTorrent
[8], FreeNet [9], FreeCache [10] and Coral [11] are used to
distribute the representations and/or the profiles. It is preferred
to use BitTorrent to distribute cluster files, where the clusters
contain the profiles.
4) Further Note on Scope.
[0093] It must not be construed that the scope of the invention is
limited to the specific examples which are listed here for
explanatory purposes. The requirement is that profile
representations are retrieved in an order such that the nearest
neighbor candidate taste profiles tend to be at least as similar to
a taste profile of the target user according to a predetermined
similarity metric as are subsequently retrieved ones of said
nearest neighbor candidate taste profiles. The intent is not to
carry out the impossible task of listing every possible way to
achieve that. The intent is to teach a number of ways to achieve
that end; other techniques that achieve that end are equivalent for
our purposes. That is, such techniques are interchangeable in the
sense that they will result in an embodiment of the invention that
falls within the scope.
NEAREST-NEIGHBOR: A target user profile's nearest neighbors are the
other user profiles whose taste profiles are closest to the target
user profiles according to the predetermined similarity metric.
However in preferred embodiments there are exceptions: users can
cause entries to be added to the nearest neighbor list that may not
be ones that have the most computed similarity, and they may delete
entries from the list, and they may cause an entry to become
permanent (though manually deleteable). They can do these actions
manually or through automatic means such as a program that runs
through one's email address book and makes the user profiles
associated with email addresses found there permanent. Such
features may detract from recommendation accuracy while adding to
the user's pleasure in the nearest neighbor community.
NEAREST-NEIGHBOR BASED RECOMMENDER: Nearest-neighbor-based
recommendation algorithms are well-known in the literature. See for
example, [13] and [14]. The source code file recommenderclass.py
described Appendix 4 and included in the computer program listing
appendix also includes a technique.
[0094] The scope of the present invention should not be construed
as limited to any particular nearest-neighbor-based recommendation
algorithm. They are fundamentally interchangeable for the intents
and purposes of the invention, although some will have better
accuracy than others. The currently preferred technique is given in
recommenderclass.py.
SERVER: The term "server" as used in this specification means one
or more networked computers, incorporating a central processing
unit and temporary storage such as RAM and also persistent storage
such as hard disks. They perform central functions such as storing
a central list of users. While there may be more than one server,
they usually do not have to be separately accessed by
user-associated computers; rather they present a unified interface.
One such example of multiple servers working together is the case
of a server computer running software that interacts with client
software running on user-associated computers, which uses other
computers for database storage and to provide database redundancy.
USER NODE: The computer (also referred to as the "machine")
associated with a human user of the computer, providing one or more
input devices such as a keyboard and one or more output devices
such as LCD screen. It is networked, preferably through the
Internet, to other user nodes. A common protocol such as TCP/IP is
used for communication with other user nodes. NEIGHBOR-FINDING USER
NODE: In currently preferred embodiments, all nodes are essentially
the same and play the role of "neighbor-finding user nodes"; but in
some embodiments, certain tasks are relegated to certain of the
user nodes. For instance, it may be that certain users are willing
to make their computational and bandwidth resources available to
others, and that others are less willing; for instance those who
are willing may get a price break.
[0095] In such embodiments, neighbor-finding user nodes take it
upon themselves to do work for multiple users. For purposes of
neighbor-finding, they work either independently of the user nodes
they are helping or in concert with them. For instance, they may
receive the candidate nearest neighbors for other users, and use
their taste profiles to compute the similarity according to the
similarity metric, and then pass on only the most similar nearest
neighbors to the user nodes across the network.
IDENTIFIERS FOR DISPLAY: Identifiers of items and nearest neighbors
are displayed in such visual constructs on a visual computer
display as tables in a window or menus such as pop-up menus. Some
embodiments may use audio means as a kind of display when visual
display is not possible. The identifiers may be identifiers used
internally to keep track of the items and users, or they may be
special public identifiers supplied by the users or item producers,
or any other identifier that is thought would be convenient for the
users.
[0096] Notes
[0097] While this specification focuses on the example of music
recommendation and communities, that is for purposes of example and
ease of explanation only. It applies just as completely to other
domains, such as books, web logs, web sites, movies, news,
educational items, discussion groups, and others. Embodiments in
all of these domains and other domains which could benefit from
taste-based recommendations and communities. Occasionally in this
specification the word "item" is used inclusively to represent the
various types of objects of taste or interest.
[0098] The word "taste" as used in this specification should not be
construed to imply that the invention's scope is limited to
artistic works. It applies equally well to information such as news
sources. The word "interest" should be considered a synonym for
"taste" for purposes of this specification.
[0099] Other information besides the taste profiles may be used in
finding nearest neighbors. As one example, some embodiments allow
the list of nearest neighbors to be restricted to individuals who
live in particular physical localities.
[0100] The specification sometimes uses the word "machine" as an
equivalent for "computer."
BRIEF DESCRIPTION OF THE DRAWINGS
[0101] FIG. 1 is an overall flowchart illustrating an embodiment in
which each client node is responsible for determining its own
user's nearest neighbors.
[0102] FIG. 2 is a chart showing how the nearest neighbor list 110
is put to use
MODES FOR CARRYING OUT THE INVENTION
[0103] FIG. 1 illustrates an embodiment in which each client node
is responsible for determining its own user's nearest neighbors.
Representations of user profiles and associated user identifiers 5
are provided in order of likely similarity to the user. See, for
example, the descriptive text for clusterfitter.py in Appendix 4,
which describes a way a client node can determine the order in
which to download each one of a set of clusters. (The source code
itself appears in the computer program listing appendix.) In the
preferred embodiment, these clusters are downloaded with the help
of other client nodes using BitTorrent. In the preferred embodiment
there are a limited number of clusters, retrieved by each client
node in its own appropriate order. Not every cluster is retrieved
by every client, because only a certain amount of time is available
to do the downloads. But on the whole, each can generally, in time,
be found on a number of client nodes. This enables a BitTorrent
tracker running on the server, together with BitTorrent client
software running on the clients, to work together to share the
community bandwidth to download a cluster to a client that requests
it. A programmer of ordinary skill in the art will readily see how
to use BitTorrent client software, publicly available in
open-source form (http://bittorrent.com/) to accomplish these
tasks. Note that there is also an existing BitTorrent "trackerless"
option that does not require a tracker on the server, but rather
distributes the tracker functionality to the nodes, further
diminishing the bandwidth load on the server.
[0104] This disclosure contains several additional sections, each
designated as an Appendix, and together with the rest of the text
and computer code presented herein, forming a unified disclosure of
the present invention. As one alternative way of achieving the
desired ordering of profiles see the distributed profile climbing
technique described in Appendix 3.
[0105] The profiles are received at the user nodes 20a-c. The
similarity of each one to the local user is calculated 30a-c. The
ones that are similar enough 40a-c to the current user (for
instance, by being more similar than the least-similar current
member of the nearest neighbor list) are put into the appropriate
position 50a-c in the nearest neighbor list. In preferred
embodiments that position is consistent with an ordering by
similarity.
[0106] In FIG. 2 the nearest neighbor list 110 is put to use.
Combined with the local user profile 120, recommendations are
generated 130 for the user (see, for example, recommenderclass.py,
described in Appendix 4 and included on the computer program
listing appendix for an example of how to accomplish that).
[0107] Interactive communications are also enabled 140. For
instance, preferred embodiments display the user identifiers of
nearest neighbors in a list on a computer display. An interaction
means such as clicking on a particular icon enables an email to be
automatically generated addressed to the neighbor and indicating
that the sender is the current user; the user then fills in the
message text and sends it.
BIBLIOGRAPHY
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No. 5,884,282
APPENDIX 1
[0126] This appendix describes a number of variations which we
consider to be part of the invention. [0127] Some embodiments of
the invention use "playlist sites" or "mp3 blogs" or "music blogs"
to supply profile information, rather than, or in addition to,
profile information stored on a local disk such as the XML database
generated by Apple's iTunes product. In typical embodiments this
information is collected by a "screen scraping" procedure, either
by a process or processes running on the server system, or on user
nodes. In some cases, such sites publish song information using
OPML or other XML formats such as RSS, which reduces or eliminates
the need for screen scraping. For embodiments making use of this
capability, profile information will be provided to users of the
system that may represent the tastes of other individuals who are
not users of the system. To a large degree, the data associated
with these individuals is treated identically to the data
associated with users. In some aspects it will normally not be
possible to treat them identically because less data will be
available for them. The adjustments that need to be made in such
cases will be readily apparent to the software developers. Note
that since this specification focuses primarily on users of the
system, there will be cases where the term "user" should be
considered to also include "ghost users" derived from external data
representing non-users. [0128] Another source of ghost user data is
services such as audioscrobbler that make identifiers of songs
currently being played by a given user available on the Web. One of
ordinary skill in the art will immediately see how to monitor such
a service to build up a profile, over time, of users whose
currently-played-song is displayed. [0129] Some embodiments provide
a facility whereby simply loading a web page (and optionally giving
permission for security reasons) will cause software to be
automatically loaded into the user's machine that provides the
necessary functionality; this avoids the separate step of
downloading and installing application software. This can be
accomplished, for instance, by means of Java-language code called
by a Web browser. [0130] Preferred embodiments have a "permanent
neighbors" feature, as well as a "machine-generated neighbors list"
feature. The machine-generated neighbors list displays identifiers
for those users that have been determined to be very close matches
in taste or interest to the current user. The permanent neighbors
list displays identifiers for users that have been selected by the
current user. [0131] In preferred embodiments, user-interface
techniques are provided for turning machine-generated neighbors
into permanent neighbors. Typically this is done by a drag features
where a member of the displayed list of machine-generated users is
dragged to the displayed list of permanent neighbors. Other
techniques include allowing the user to select member of the
displayed list of machine-generated users and call a menu option to
cause it to be listed as a permanent neighbor; this can be a pop-up
menu, a contextual menu, or a standard menu. [0132] Permanent
neighbors may be manually removed from the permanent neighbors list
by the user; for instance, by means of a menu choice or drag
operation. Another option is a checkbox where multiple permanent
neighbors can be marked for removal, accompanied by a separate
button to cause the removals to happen. [0133] In preferred
embodiments, UI elements are provided to enter an email or IM
address for an individual, and cause him to be emailed such that
the said email includes a link (or other technique) for enabling
easy download of client software implementing the invention. In
further embodiments, the other user is automatically added to the
permanent neighbors list when the other individual becomes a
registered user of the system. This may be accomplished in many
ways, readily discernable to one skilled in the art; the scope of
the invention should not be construed as being limited to the
examples listed in this paragraph; they are listed for reasons of
example only. For instance, as the user profiles arrive on the
client machines for determining which are nearest neighbors, they
can be checked to determine whether an emailed individual is among
them. (The addresses of emailed users would be stored on the local
user's machine for this purpose.) Alternatively, the client can
periodically query a database table residing on the server, to
check whether the emailed user has become a registered user. [0134]
In preferred embodiments, permanent neighbors can include ghost
users, where the ghost users are identified by the local user by
appropriate network identifiers. For instance in the case of online
playlists, a URL that identifies the playlist of the particular
individual would be one appropriate type of identifier. In further
embodiments, the data for such neighbors is retrieved directly
(across the network) by the client node without interaction with
the server that implements the server portion of the invention.
[0135] In preferred embodiments, users may click on the identifier
for a permanent neighbor and cause information to be displayed that
represents the user's musical tastes; such as a list of artists
and/or songs in the user's collection, possibly including such
elements as the number of times each song has been played, the date
added to the collection, and others; this list is for example only
and not intended to be inclusive. Further embodiments display this
data for permanent users in the same onscreen list area that is
also used for displaying the analogous data associated with machine
generated neighbors. [0136] In preferred embodiments where
neighbors are used as the basis for generating recommendations, it
is recognized that permanent neighbors may or may not be the ideal
individuals to generate recommendations from. For instance, an
individual may be made into a permanent neighbor because he is a
friend, rather than because his tastes are remarkably similar to
those of the local user. Accordingly, in such preferred embodiments
the option is provided to leave permanent neighbors out of the
recommendation process. In some such embodiments, this is done as a
single binary choice for all permanent neighbors, for instance,
using a checkbox that appears in a Preferences dialog. In others,
it is done on a one-by-one basis, for instance, with checkboxes
accompanying each listed, displayed identifier for permanent
neighbors in the user interface. In some embodiments, it is
possible to make a single binary choice to indicate that only
permanent neighbors are used for recommendations; in others there
is a screen widget such as a collection of 3 radio buttons or a
standard menu which "sticky" indicators of a previously made
selection, where the user can choose between not using permanent
neighbors in the recommendations processing, only using permanent
neighbors, or using both. [0137] Preferred embodiments display the
most recent date and/or time that each permanent or
machine-generated neighbor last used the system, to the extent that
the client may be easily aware of that information. For instance,
it may be included in profile information that arrives at the local
user's node for processing of candidate neighbors; in which case it
may not be the most recent data available to the system as a whole.
Alternatively it is retrieved directly from the server when it is
to be displayed, and is thus up to date. [0138] Preferred
embodiments contain on-screen lists of neighbors (which may include
permanent neighbors or where permanent neighbors may be in
separate, similar lists); in further preferred embodiments these
lists contain screen elements of the presence or absence of email
addresses for the users (needed because, in preferred embodiments,
it is optional to supply an email address and/or to allow other
people, preferably including other users, to be made aware of
them). In further embodiments, clicking on such an element causes
an email application opened and an automatically-addressed email to
be generated, to be populated with content by the user. Similarly,
elements indicating an IM address, or other communications handles,
may be displayed, and UI functionality provided to facilitate such
communications. In some such embodiments one element is provided
for each neighbor to indicate one or more than one modes of
communication as available, and clicking it causes a menu to appear
that lists them; choosing one facilitates communication by the
chosen mode. In other embodiments, the user selects the list row
containing the user identifier, and brings up a standard menu to
choose a mode to communicate with the selected user; when
communication handles are not provided for a particular mode, that
one is greyed-out. A software developer of ordinary skill will
readily see other variations of how to facilitate user interaction
regarding what modes are available and how to facilitate engaging
each one. Such variants which contain some on-screen indicator of
the availability of communications with a given user are within the
scope of the invention. Software developers of ordinary skill in
the art will immediately see how to implement this. [0139] In
preferred embodiments certain individuals are registered as being
artists. When an item such as a song by such an individual is
displayed on screen, and if the artist has indicated that he wishes
communications with him to be enabled, an indicator of that is
provided, and UI techniques for facilitating such communications
are provided; these techniques will generally be similar to those
already discussed for user-to-user communication. Software
developers of ordinary skill in the art will immediately see how to
implement this. [0140] When artists communicate with users,
preferred embodiments monitor the uniqueness of the communications,
in an attempt to determine whether artists are really communicating
one-to-one with users. One way to determine this would be to
randomly sample a number of pairs of communications from artists,
and use "diff" text comparison techniques to compare them. Artists
with low average number of differences are considered by the system
to not be truly engaging in one-to-one communications. Other
techniques that enable some measure of general uniqueness to be
determined also fall with in the scope; the invention is not
dependent on any particular technique for that functionality. In
various further embodiments, there are ramifications of being
considered to not engage in true one-to-one communications; for
instance, in some embodiments, such artists are banned from being
presented to users as potential targets of communication; in others
there is a displayed list of artists who appear to tend to use
"canned" responses; in others that individual is not enabled to
initiate communications with non-artist users. In preferred
versions of such embodiments an artist can denote a particular
communication as being an announcement, and it would then be
excluded from the described uniqueness checking. [0141] Some
embodiments provide UI functionality that allow the user to specify
a genre or artist or other criteria for determining a subset of
items, and then causing item recommendations to be selected from
that subset. [0142] Some embodiments enable recommendations to have
their order at least partly determined by the similarity of the
item to the items associated with some specified artist(s),
item(s), or other grouping of items (such as an album of songs).
[0143] Some embodiments provide professional-interest-matching or
dating services by examining files on the user's local computer,
for instance words in documents, and possibly words in linked URL's
where the links themselves are stored on the user's computer, to
build interest profiles; neighbors and, in preferred such
embodiments, item recommendations, are based on this data. [0144]
Some embodiments use a bar-code reader or other automatic means for
identifying physical objects in order to generate, or as a
contribution to, the data in the user's taste profile. For
instance, music CD cases typically have bar codes that can be used
for that purpose. (Note that a software product for the Mac OS X
operating system, called Delicious Library, has the ability to take
data supplied by a bar code reader to build a digital library of
physical CD's and other items; however it has none of the other
features described in this invention.) [0145] Some embodiments add
a gift suggestion feature. The individual for whom gift
recommendations are to be made available makes his relevant data
available to the machine associated with the user who wants to give
a gift. For instance, in one such embodiment, an iTunes user might
email his iTunes Music Library.xml file to the user who wants to
give him a gift. Other techniques for getting the relevant
information to the local user are equivalent from the standpoint of
the invention. Then local processing occurs for that other user's
data that is basically the same as for the local user's own data.
For instance, in embodiments involving recommendations by means of
neighbors, a collection of machine-generated neighbors is found
relative to the gift recipient's data, and recommendations are
generated from that and displayed on the screen. The value of this
is that the local user already has the code necessary for such
functionality, for his own recommendations; and in this case much
of that same code is re-used for purposes of gift suggestions.
[0146] Some embodiments interact with an online music store in such
a way that highly recommended music is automatically purchased at
regular intervals of time. For example, on a monthly basis, an
embodiment that works with the iTunes music store could cause the
most recommended n songs where n is 1 or some greater number to be
automatically purchased and downloaded to the user's machine. In
preferred embodiments, the user is alerted before this occurs and
given the choice to modify the list of songs to be purchased; for
instance, the application software might display an alert dialog,
the day before the purchase is to be made, which indicates that the
top 10 songs will be purchased; input means such as checkboxes next
to the listed songs may be used to indicate that certain songs
should excluded from the automatic purchasing. Preferred
embodiments allow the user to choose the periodicity and number of
songs to be automatically purchased. In some embodiments, this
process is used to cause the creation of a physical CD by a store,
containing recommended music (or, in other embodiments, videos,
books, etc,), which is subsequently shipped to the user. [0147]
Preferred embodiments give the user control over which artists are
considered to be part of the user's effective taste profile. For
instance, in one embodiment the local user can view a list of the
artists in his music collection; there is a checkbox next to each
one, defaulting to checked; if it's unchecked, that artist is
effectively ignored in other processing based on the taste profile.
In the embodiment in question, this is accomplished by means of a
tuned taste profile and untuned taste profile; the only real use of
the untuned one is to present that list to the user for tuning by
unchecking checkboxes. So, in embodiments providing control over
the artists that are considered to be effectively part of the taste
profile, where the user's local taste profile is used for finding
nearest neighbors, only the desired artists are used; and where
taste profiles are part of the data broadcast by the system to be
viewed by other users and/or for other users to choose neighbors,
only the desired artists are used. In some embodiments different
sets of artists may be chosen for finding neighbors of the local
user and for broadcasting, but preferred embodiments combine those
features. A software developer of ordinary skill in the art will
immediately see other ways of handling the user interface and
technical issues for achieving the same purposes; these are
equivalent from the standpoint of the invention.
[0148] One embodiment involves online chat. An interest profile is
built based upon a) the words the local user types into his chat
client and/or b) the words that appear in messages typed by other
people into the same chat room. In the case of (b), a subset may be
used where the only messages that are at least somewhat likely to
be responses to messages from the local user are used--for instance
by distance in time from the time of a message sent by the current
user to the chat room where the potential response appeared. By
collecting these words over time (and in some embodiments, giving
words posted by other individuals less weight), a profile of chat
interests can be built for each user. Then, when the system builds
neighborhoods of similar users, those neighborhoods can be viewed
as potential chat partners. In preferred embodiments a user clicks
on a user identifier to start a chat session with them. In some
embodiments chat rooms are automatically initiated for groups of
similar users. In chat embodiments, no other recommendations are
necessary. Note that variants of this set of embodiments use
different techniques to match people together according to the
words they type. The simplest way is to simply treat the words used
by a user as a document; then techniques for document similarity
which take word frequency into account can be used. (A search on
Google for "document similarity" will bring up numerous
techniques.) But any technique that calculates useful similarities
based on the word content is equivalent for purposes of the
invention. [0149] Some embodiments provide means to restrict
candidate neighbors by certain criteria such as physical locality.
One way to do this is to simply assign the lowest possible
similarity to people who don't meet the restriction requirements;
another is to exclude them at the outset from the
neighbor-searching process. Techniques to do this will be
immediately apparent to a software developer of ordinary skill.
[0150] One advantage of having software running in user nodes is
that certain parameters for recommendation quality can be tuned on
the user node, for the given user, by computationally expensive
techniques such as genetic algorithms. Some embodiments take
advantage of this fact by using iterative testing, genetic
algorithms, simulated annealing, or other optimization techniques
to tune parameters such as the following: the number of neighbors
to use in recommendation calculations (assuming only the must
similar neighbors are chosen), the optimal adventurousness (see
elsewhere in the specification for discussion of adventurousness),
a cutoff release date for recommended items (for instance, the user
may not be interested in old music), and others. One such other is
a number representing the lowest weight to be associated with any
user's information; the least similar of the nearest neighbors is
assigned this weight and interpolation, with a max of 1 for the
single nearest neighbor, is used to assign weights to the other
neighbors according their rank or another measure. The optimization
may be based on tuning the parameters to get the best match between
recommended music and the music actually already in the user's
collection. (Obviously under normal processing, preferred
embodiments do not recommend music that the user already has, and
this screening is disabled for optimization purposes.) Preferred
embodiments in the music domain try to optimize the match between
ranks based on song plays per day and order of recommendation. For
instance, Spearman's Rank Correlation can be used to do this. Some
tuning operations may change the number of recommended songs; to
find the optimal setting it may be useful to compute the p-value
associated with each pair of rankings; the more statistically
significant the p-value, the better. When rank correlation is used,
preferred embodiments only consider the ranks of the top
recommendations, because we are less interested in the exact rank
of songs that are not particularly recommended. At an extreme of
this general approach, some embodiments uses Koza's Genetic
Programming technique to generate at least part of the algorithm
used in the recommendation process, using similar fitness criteria
to the optimization measures mentioned already in this paragraph.
[0151] In embodiments which carry out evolutionary computation like
genetic programming, the invention has useful ramifications for
multiprocessing. For instance a each user node evolves chromosomes
(such as hierarchical programs in a genetic programming
environment) which best suit the needs of the local user. It is
likely that those same chromosomes will be relatively
high-performing for other users who have the local user among their
neighbors. So in preferred evolutionary computation embodiments,
one or more of the highest-performing genomes that has resulted
from the evolutionary process on a user node becomes part of the
profile, which also includes the taste profile. Then other user
nodes that select a particular user as a neighbor will also have
his highest-performing genome(s) available. These can be used
directly; combined with those supplied by other neighbors by (for
example) averaging the recommendation strength for each song across
all genomes, or seeded into the evolving population of genomes on
that node; this is a form of multiprocessing evolutionary
computation. It should not be construed that the invention is
limited to the example of multiprocessing evolutionary processing
described here; it is an example only. For instance literature on
genetic programming is rich with research on ways to do genetic
processing in a multiprocessor environment. Those skilled in the
art of genetic programming will see numerous ways to leverage the
fact that each user has a neighborhood of users who will tend to be
well-served by many of the same genomes, and have user nodes that
are available for multiprocessing to better serve the needs of all
such users. For instance, without restricting genomes that are fed
from other users into a local user's genome population to just the
set of nearest neighbors, some embodiments give more or less
probability to a foreign genome being added commensurate with the
other user's similarity to the local user. Many variants of taking
advantage of the similarity information and the overall structure
of the invention will occur to those of ordinary skill in the art
of genetic programming, and it must not be construed that such
variants are not within the scope of the invention; that is,
variants are within the scope if they result in better performance
due to the following attributes of the invention: a) the fact that
the mechanism that transports taste profiles from user node to user
node (which may involve using the server as an intermediate step)
can also be used to transport genomes, either as a separate data
package or as part of the same data package, and b) that mechanism
is set up so that profiles with a higher similarity to the local
user have a higher probability of arriving sooner (and, in some
embodiments, at all), and those genomes are more likely than
randomly-chosen ones to have higher fitness for the local user.
[0152] In preferred embodiments, direct peer-to-peer communication
of individual taste profile information occurs between neighbors.
This can enable faster updating of neighbor taste profile data than
would occur through the usual mechanism described in this
specification. Further embodiments provide an output mechanism for
showing identifiers of the digital item currently being experienced
by other neighbors; in some such embodiments that information is
also used to update the neighbors' taste profiles stored on the
local node while waiting for full updated taste profiles to arrive
through the usual mechanisms. In preferred embodiments which make
use of peer-to-peer techniques as described in this paragraph, the
fact that some nodes may be behind firewalls that prohibit incoming
connections from being made are handled by sending the necessary
data through other nodes that do have the necessary ports open. Any
software developer of ordinary skill in the art of peer-to-peer
network programming will immediately see how to create the
necessary peer-to-peer mechanisms for the functionality described
in this paragraph; it should not be construed that only particular
implementation mechanism are within the scope of the invention.
[0153] In preferred embodiments, users can create different taste
profiles for themselves which fit different moods or interests.
Most or all of the overall mechanism described in this
specification then applies to each separate taste profile.
Neighbors are found and recommendations are generated for each one.
For instance, playlists generated in Apple's iTunes program can
comprise music taste profiles. [0154] In some embodiments at least
some users run a special version of software that implements the
invention, in which not all the usual user interface features are
necessarily present. In these embodiments, certain musical tracks
are indicated as being free of charge--for instance, in the names
of the files, or in a database. The user is recommended a
collection of free songs. Identifiers for the songs are then
uploaded to server system (not necessarily the same one as for
other functions). Then the free songs are copied into a portable
music player from a computer that is networked to the that server.
Then the portable music player is packed and shipped to the user.
Facilities are provided where there is a web site where the user
orders and pays for the player, and is informed about how to get
the software that will make the recommendations. In preferred
embodiments a list of recommended free songs is presented to the
user and the user can choose which ones he wants; identifiers of
the chosen songs are sent to the server. Networking software and
online store developers of ordinary skill will immediately see
numerous ways to implement the required functionality; various
implementations are equivalent from the standpoint of the
invention; the scope of the invention is therefore not limited to
certain implementation techniques. Note that this functionality may
be removed from other aspects of the present invention;
recommendations may be wholly made on the server based on data that
is input, via the Web, to the server, using any recommendation
methodology; the recommended songs are then loaded onto a portable
players and shipped as described. [0155] Some embodiments which
involve artists having special accounts enable chat rooms for each
artist, and provided indicators in the UI associated with artist
names (such as next to the artist names in a list of artists) that
show whether they are in the chat room or not, and means are
provided for the user to click or otherwise interact with an
onscreen control to cause them to "enter" the artist's chat room
and chat with the artist. Practitioners of ordinary skill in the
relevant programming techniques will immediately see numerous ways
to implement this and these are equivalent from the standpoint of
the invention. [0156] In some embodiments special taste profiles
are created that are structured like user taste profiles, but
actually are taste profiles for an item. For instance in an
embodiment which calculates user similarity based on the musical
artists they have in common in their libraries, taste profiles are
manually created for certain songs (such as songs that are
sponsored by commercial interests) that mimic user taste profiles
in the sense that each one contains a list of artist. Then the same
similarity-calculating code can be used to find the songs that are
most similar to the current user, and these may appear in a special
recommendation list or mixed in with other recommendations
GLOSSARY FOR APPENDIX 1
[0156] [0157] User Nodes--machines that are on the network that
also directly interact with users; typically these are machines
owned by users or associated with them at their work locations.
[0158] Screen scraping--a software process that reads an HTML (or
other) page on the World Wide Web (or other network system) that is
intended for human use, and extracts useful data from it for
machine use. [0159] Ghost user--data representing an individual
that is derived from an external source such as a music blog. In
many ways, ghost users may be treated identically to regular users
of the system. [0160] Current user, Target user, Local user--these
terms represent the user who is running software which implements a
client portion of the invention; typically he or she is the one
that is recommendations and one or more lists of neighbors are
associated with in the course of examples in this specification.
[0161] IM--instant message, typically associated with chat
software. [0162] Neighbor--may be used to indicate
machine-generated neighbors and/or permanent neighbors. Note that
different embodiments may use different terminology for these.
[0163] Nearest neighbors--the set of neighbors who are most similar
in taste to the local user; normally the same as machine-generated
neighbors; though it is not impossible that a user will manually
find a neighbor that is actually more similar in taste than the
machine-generated ones, and add him to permanent neighbors. [0164]
Artist--a creator of items of interest to the subject domain or one
of the subject domains of an embodiment of the invention. We use
the term for shorthand, and, for example, in some domains such as
academic papers, it could refer to an academic who wrote or
co-wrote such a paper. [0165] UI--user interface. In most cases,
the user interface will involve a computer with a CRT or flat-panel
screen and a keyboard, displaying a windowing system such as
Microsoft Windows or Mac OS X. Such systems normally provide
standard means to create menus, lists (or tables), checkboxes, etc.
In other cases the UI may be audio with input by means of telephone
touch-tones. The requirement is that it provides functionality that
facilitates human-computer interaction. [0166] Item--an item is the
basic unit of content, such as a song [0167] Interest profile or
taste profile--data which is indicative of the interests or tastes
of a user. Often used interchangeably in this specification. For
instance, digital music user will normally have identifiers of the
songs he likes (or that are in his collection) in his taste
profile. [0168] Server--a server is a central computer, or
networked group of central computers that handle certain tasks for
the benefit of the client nodes, such as storing a database
containing login ID's, passwords, and profiles.
APPENDIX 2
[0169] This appendix describes another description of key
functionality of the invention, including but not limited to
facilitating retrieval of representations of nearest neighbor
candidate taste profiles and associated user identifiers in an
order such that said nearest neighbor candidate taste profiles tend
to be at least as similar to a taste profile of the target user
according to a predetermined similarity metric as are subsequently
retrieved ones of said nearest neighbor candidate taste
profiles.
[0170] This description is from U.S. provisional patent application
60/540,041, filed Jan. 27, 2004.
[0171] The specification describes a product named Goombah.
However, the focus on Goombah is for clarity and descriptive
purposes only and it must not be construed that the scope of the
invention is limited to that particular embodiment or to the field
that Goombah operates on (music).
[0172] Goombah's first purpose is to build a list of "nearest
neighbors" for each user. They then form a community of like-minded
people for communication purposes, and they also form a source for
recommendations of items--if you have extremely similar tastes to
me and you have an album I don't have and you play it all the time,
I should probably give it a try. So that's the basis of the
recommendations.
[0173] To find nearest neighbors exactly correctly is an O(N 2)
problem if simple technology is used, and we hope to have hundreds
of thousands or millions of users whose profiles are constantly
being updated, so we wanted to do better than O(N 2).
[0174] There are probabilistic nearest neighbor algorithms that
reduce this complexity hugely, but at a loss in reliability in
finding the true nearest neighbors. We wanted to do better.
[0175] The key idea behind Goombah, whose purpose is to solve the
above problem, is that the computations for finding the local
user's nearest neighbors are carried out on that user's machine.
So, if we have a million users, we have a million CPU's doing the
work of finding nearest neighbors.
[0176] There are three reasons why such an approach is within now
the realm of feasibility, where it wasn't a few years ago:
1) Most people who are heavy users of digital music have high-speed
Internet connections, otherwise it would be unpleasant to do
downloads from the likes of Apple's iTunes Music Store. 2) New
technologies such as BitTorrent has emerged recently which offload
bandwidth concerns for sending large files from a central server to
the user nodes. In particular, the following is true for
BitTorrent: The central server has a copy of the file that people
need, but once one user has it on his machine, he is automatically
set up as a server as well, and so on for every other user.
Transfers are carried out from other users invisibly. (This is
different from something like napster where you have to choose
another user and request a download. Instead, the central server
knows where all the copies of the files are, and tells a node that
needs a copy the addresses of several machines to simultaneously
get different chunks of the file from until the whole file is
build. If a sending node drops out, other nodes automatically take
its place, and so the file is eventually downloaded from multiple
changing sources in a completely automated way.) This means it is
possible for a company like Transpose to make very large files
available to very large numbers of users without having hugely
expensive server and bandwidth needs. Furthermore, it happens that
BitTorrent is open source with a very friendly license and written
in the same language (Python) that Goombah is written in. 3) Any
serious digital music user already has a hard drive with gigabytes
of space devoted to music, so spending a 100 megs or more on the
data associated with an application like Goombah is no big deal. In
the future, videos will commonly be stored on user hard drives, so
that is another application for Goombah as it evolves.
[0177] So, essentially the idea, when a local user wants to find
his nearest neighbors, is to download the profiles of all other
users who could reasonably be considered to be candidates to be
nearest neighbors of that local user. Then, the local user's
Goombah application does a search of all those profiles to find the
best matches.
[0178] Instead of downloading individual profiles, Goombah will
download a single very large file--10's or even 100's of megs--that
contain the candidate profiles. This will happen by means of
BitTorrent.
[0179] These large files will be formed by a clustering
algorithm.
[0180] We will find clusters of similar users which are large
enough to contain most reasonable nearest-neighbor candidates for
each general type of musical taste. They will be large enough to
fill that need, and small enough to download in a reasonable time
on a high-speed connection and not take a problematic amount of
space on the user's hard drive.
[0181] So, the local user will download a large BitTorrent file
containing all nearest neighbor candidates and do an exhaustive
search on his machine for nearest neighbors.
[0182] Then he can communicate with his taste-mates and get
automated music recommendations from them.
[0183] The large file will be updated on a regular basis with
further BitTorrent downloads.
[0184] The clustering algorithm can be any clustering algorithm
that is capable of clustering a large number of users according to
their degree of interest in a large number of subject items. (Where
the degree of interest may be indicated by real-valued, binary,
integer or any other that can represent a degree of interest.)
[0185] As one example, the commonly-used C4.5 algorithm can do
this. For example, the open-source Java software WEKA has a module,
weka.classifiers.trees.j48, which implements C4.5. In the context
of using this module in a music setting, each user is an "Instance"
and the song identifiers, such as strings containing the artist
name, album name (if any), and song title, are used as the values
of a "nominal attribute" representing the songs.
Miscellaneous Notes for Appendix 2:
[0186] The step of using the local CPU to find nearest neighbors
can be conducted in various ways. Any sub-algorithm which
accomplishes the function "find nearest neighbors out of the
downloaded large file" is considered equivalent for the purposes of
the present invention. Possible ways to do it include an exhaustive
search for the other users that are most similar to the local user
according to some similarity metric. (The attached Python scripts,
recommenderclass.py and tasteprofileclass.py contain code for
generating a similarity metric. However it must be stressed that
there are innumerable ways of generating a similarity metric for
nearest-neighbor purposes, and they are all functionally equivalent
from the standpoint of the present invention and all fall within
the scope of the present invention. We can use any metric that
results in reasonable likelihood that two users that are considered
more "similar" than another pair of users actually have more shared
interests in the targeted interest-domain [such as music] than
another pair of users with lesser similarity. Note further that we
aren't using the word "metric" in its most rigorous sense, but in
its general sense as a quantity used for measurement and
comparisons.)
[0187] Another way to find the nearest neighbors from the
downloaded large file is to use the vp-tree technique introduced by
Peter N. Yianilos in his paper "Data Structures and Algorithms for
Nearest Neighbor Search in General Metric Spaces". The large file
to be downloaded would be formatted as a vp-tree and thus very fast
nearest-neighbor searches would be facilitated on the local
machine. Again, any technique used to find the nearest neighbors is
functionally equivalent from the standpoint of the invention and
falls within the scope of the invention.
[0188] The step of using peer-to-peer techniques for downloading
the large files can also occur in various ways which are
functionally equivalent from the point of view if the current
invention. In fact, the invention does not depend on any particular
technique for getting files from peers and all such techniques
should therefore be considered functionally equivalent from the
point of view of the invention. For instance, while BitTorrent
provides a particularly compelling model for how this may be
accomplished, the Gnutella provides an alternative model.
[0189] A difference between the BitTorrent and Gnutella approaches
is that with BitTorrent, each file has a distinct URL which is
understandable by a server machine which runs BitTorrent "tracker"
software. By means of this URL, client software is told by the
tracker which peers store the file (or parts of the file) so that
the client can cause downloads to be started from a subset (or all)
of those peers. With the Gnutella approach, there is no central
server, and the local computer sends queries into the "cloud" of
known peers and machines known to those peers, looking for files
with particular filenames. Then, normally, one of those peers is
chosen to be the source of the download.
[0190] The commonality between all these various techniques is that
the large files each represent a group of similar profiles (or,
alternatively, all available profiles), there are a fixed set of
such files at any point in time, and the user causes one (or more)
to be downloaded that is (are) particularly likely to contain
worthy nearest neighbor candidates; these files are usually
downloaded from one or more peers rather than from a central
server. All techniques which satisfy these requirements are
functionally equivalent from the perspective of the present
invention and thus fall within the scope.
[0191] One key step is determining which large file a particular
client should download in order to meet the needs of its user. Of
course, in embodiments where all the profiles are in one large
cluster, there is no issue. When they are divided into clusters,
and each cluster is represented by a particular large file,
however, this step needs to be carried out.
[0192] One way to accomplish this step is as follows:
[0193] When a system is first set up to embody this invention, it
will usually only have a relatively small number of users on Day 1.
Thus, there is no need to divide the population into separate
clusters for downloading. As the user population grows in size, a
single file is used for download purposes.
[0194] Finally a point may arrive at which it is deemed, due to the
relative of expense of bandwidth and diskspace, that the user
population should be divided into two clusters. At that time, a
clustering algorithm is run and the user population is divided into
two clusters. Each of the two clusters is given a name: for
instance, "U0" and U1".
[0195] Now, as time goes on, we do not regenerate those clusters
from scratch. Rather, as new users are added to the system, they
are added to the most appropriate cluster. This may be done in any
number of ways. A centroid for the cluster may be calculated, and
the new user added to the cluster whose centroid it is most similar
to. Or the average similarity between the user and each cluster
member may be calculated for each candidate cluster, and the most
appropriate cluster chosen on that basis. Or, the change in entropy
that would arise in the system as a whole due to each possible
choice of cluster can be calculated, and the choice taken that
minimizes the change in entropy. Any of these techniques, and all
other techniques that cause the user to be placed in one of the
existing clusters, are functionally equivalent from the point of
view of this patent as long as they have put the user in a cluster
that is highly likely to result in a good degree of similarity
between the new user and other members of the cluster.
[0196] In this way, clusters have consistent meaning over time, and
the user can stay in the same cluster, until a further split is
deemed necessary. In preferred embodiments, this is handled by the
expected large file simply not existing at a particular point in
time, and this is detected by the client, which thus assumes it
needs a new cluster assignment. It then queries the server system
for a new assignment. For a pre-existing user this is easily
determined because the new assignment was made during the split
process, so the server returns another cluster identifier
consistent with that split. For example, if a user was in cluster
U0, he may now be in cluster U01 (where the leading 0 represents
the lineage). (Of course any cluster naming convention can be used,
but preferred ones encode the lineage in the name).
[0197] Other embodiments which use a fast enough clustering
approach regenerate the clusters from scratch on a regular basis.
In such embodiments the client either requests a new identifier for
the cluster file, or one is sent automatically by the server when
the client and server are in communication. (Note that this
communication can actually take a number of forms. Rather than
sending text strings, numeric or other identifiers can be sent
which are in turn used by the client to build the necessary handle
to access the file. Two examples: In a Gnutella-style system, this
handle would probably be a search term. In a BitTorrent-style
system, the handle might be the URL for the torrent.)
[0198] Still other embodiments have relatively stable clusters but
continuously work to refine them by moving users from one cluster
to another if such a movement provides superior clustering. For
instance, periodically each user may be considered again as if it
were a new user, and a decision made about what cluster it should
go into. If it changes then that will be reflected in future
communications between the client and the server (although the
change does not need to be reflected immediately).
[0199] In some embodiments, the client has no persistent
"knowledge" about what cluster the user is in, and when it's time
to get a new cluster, queries the server for the information
required to start a download of the appropriate one.
[0200] In some embodiments, users may be assigned to more than one
cluster. As one example of how that might be done, a number of
standard clustering approaches such as C4.5, assign probabilities
for cluster assignments; thus a user might with a higher
probability reside in one cluster than another. It would be
possible to take the two clusters with the highest probability for
a given user, and say that he resides in both of them. The
invention is not limited to any particular approach to putting
users in more than 1 cluster. The functionality is simply that the
user would go in the clusters that provide a high match to his
interests, and any technique that accomplishes that is functionally
equivalent from the perspective of the present invention and is
therefore within the scope.
[0201] In some embodiments, different clustering arrangements exist
for different genres. For example a user who has both classical
music and jazz in his collection might benefit from different
nearest-neighbor communities generating different recommendations
in each area. So, the entire clustering and downloading structure
and steps, in some embodiments, are carried out more than once. In
other (preferred) embodiments, each user still is in only one (or a
small group of) cluster(s), but his client software finds different
nearest neighbor sets, depending on genre, from within those
clusters. Of course, in non-music applications, this concept is
extended by means of the analogous principle to "genre" that exists
in that other subject area. For instance, if the items are weblogs,
then an individual might be interested in weblogs about Perl
scripting and also weblots about Republican politics. These
different subject areas are handled analogously to genres in the
music world.
[0202] In order for the system to respond to the needs of users who
are continually buying new music (viewing new weblogs, etc), in
preferred embodiments it is possible for neighborhoods to be
updated according accordingly. This means that the large files
representing clusters need to be either re-downloaded or updated
periodically. We will discuss below some of the ways this is
accomplished in various embodiments. The scope should not be
construed to be limited to these particular techniques. Rather, any
technique that "enables the potential neighbor files to be updated
or replaced often enough to increase the accuracy and pleasure in
using the system" equally fulfils the required function and is thus
considered to be in the scope.
[0203] In some embodiments, download file identifiers (which may be
URL's, terms, etc.) are constructed based on two pieces of data:
the cluster identifier plus the date. For instance a user might be
in cluster U011. If the date is Jan. 27, 2004, the download file
identifier might be U01120040127. The client can then get an update
by, for instance, downloading the file containing that string in
its name or by constructing a BitTorrent URL based on that
string.
[0204] The client machine can then download the file upon whatever
schedule is most consitent with the user's needs and desires.
Bandwidth will be a constraint, so there is reason not to download
the files too frequently. In preferred embodiments, there is a
choice in the "preferences" section of the program whereby the user
can specify how often he wants to update the file. He will probably
do so less frequently if he has a dialup modem connection than if
he has a cable modem. Some embodiments use information available in
the computer (for instance, provided by the operating system) to
determine the connection speed, and automatically choose a download
schedule accordingly. Some ask the user to specify the download
speed and automatically choose a download schedule accordingly.
Other ways of determining a download schedule, including the user's
manually starting each download, are all functionally equivalent
and within the scope.
[0205] Some embodiments automatically cause files of different
sizes to be downloaded according to connection speed (or at the
choice of the user). One way this is done is for the server to
store a tree of cluster arrangements. For instance, suppose
clusters are arrived at by splitting bigger clusters in half, and
the lineage of the cluster is represented in the file name. Then,
for example, U0 might be the parent of U01, and U01 might be the
parent of U011. Then a client with less bandwidth available to it
might retrieve cluster U011 and one with a great amount of
bandwidth but with a user with a very similar taste profile to the
first client, might retrieve cluster U0. The difference is that the
larger the downloaded cluster, the more likely it is that the true
most similar neighbors, out of the whole universe of neighbors,
will be found by the client.
[0206] In some (preferred) embodiments it is possible to either
download a cluster as a whole, or download updates. For instance,
using the naming convention we have used above,
U01120040127-20040126 might be the identifier of the file that
contains the difference data between an up-to-date representation
of cluster U011 as it appeared on Jan. 26, 2004 and the version
that was current on Jan. 27, 2004. Then a preferred embodiment will
automatically choose whatever method will result in getting current
more quickly. For instance, if no update has occurred in a number
of days, it may be more efficient to download the complete file.
But if the last update was recent, it may be more efficient to
download a series of daily updates.
[0207] In a preferred embodiment making use of BitTorrent, the
server stores, for each cluster, files representing the current
complete cluster, individual updates for the last 6 days, and the
last 4 weekly update files (files that update for a whole week).
BitTorrent requests for any of these files cause them to be loaded
to client machines, where they are henceforth made available in a
peer-to-peer manner. Any such manner of scheduling updates is
functionally equivalent.
[0208] Those skilled in the art will know how to create such update
files. There are general "patching" software technologies, but more
particularly it is easy to create custom approaches. For instance,
if the cluster file contains a list of user ID's with each user ID
followed by a list of the songs found on his or her computer, an
update file may consist of a list of user ID's of users who
downloaded new songs in the corresponding time interval, with each
user ID followed by a list of the new songs and a list of songs
that used to be on the user's disk and no longer are. All such
representations are functionally equivalent and fall within the
scope of the invention.
[0209] Another aspect is the fact that changes on the user's
machine need to be uploaded to the server. In some embodiments this
is done on a regular schedule when there are changes to upload.
Preferred embodiments only send changes since the last upload
rather than uploading the entire interest profile. Preferred
embodiments don't send changes until sufficient changes have
accrued that it is "worthwhile" to do an update. For instance, in
embodiments where taste profiles include information about the
number of times a song has been played, it makes a big difference
when that count goes from 0 to 10, but very little difference when
it goes from 1000 to 1001. A simple way to determine significance
is to have a cutoff for the percentages involved. For instance, if
play counts are used, the if overall they have changed by 1%, that
might be considered significant. If simple presence/absence data is
used, than a 1% difference in that data might be considered
significant. Alternatively, the entropy of the data may be used.
For instance, entropy can be calculated based on the exercise of
choosing a "play" at random, and computing the probability that
such a randomly chosen play instance would arrive at a particular
song. So there is one probability for each song. Based on those
probabilities the song entropy may be calculated. Then significance
may be determined by a particular amount of change in entropy
occurring, either on a percentage basis or based on a fixed minimum
change in value. Any technique that determines that a desirable
amount of change has occurred is considered functionally equivalent
from the standpoint of the invention and thus falls within the
scope.
[0210] In some embodiments the user can determine how much
significance is required before an update occurs; in others it is
automatically determined based on bandwidth; in others it is
determined on a global basis by the server; in others some
combination is used such as a maximum upload frequency being
determined by the server with the user having the ability to set
the frequency or significance required as long as it is below the
global value; any number of other techniques are possible and
considered functionally equivalent within the scope of the
invention.
[0211] Note: Music is discussed in this specification for reasons
of example only. The invention applies to other areas just as well,
including text documents, videos, weblogs, and indeed any type of
item where user interest can be determined by means of his
association, and/or degree of association, with a number items of
potential interest. Software developers will readily see how to
create these alternative embodiments. It must not be construed that
the invention is limited to the specific examples described in this
specification.
[0212] The overall invention, in broadest form, consists of a
server (or networked group of servers) that stores the cluster
files containing interest profiles and distributes them to client
machines, and client machines that then distribute those files to
other client machines; the nearest neighbors are then chosen on
client machines and used for purposes of recommendation and
community.
[0213] Clusters should be large enough to include most users whose
profiles are reasonably likely to be global "nearest neighbors" for
any given local user.
[0214] It would be worth while to discuss one further sample
application of the technology. That is one where users are
purchasers of DVD's for viewing videos. The interest profile would
consist of the list of DVD's owned by the user (perhaps with
additional entries that are liked or particularly disliked by the
user), optionally associated with the ratings. Numerous
technologies are available for finding nearest neighbors based on
such data, such as those used by Firefly or the movie
recommendation patents of John Hey, or the present inventor's U.S.
Pat. No. 5,884,282. (All such algorithms are functionally
equivalent from the standpoint of the present invention.) This
profile data is usually manually entered by the user.
[0215] In addition to forming communities and recommendations as
already described, this embodiment adds functionality for making it
visible to other users that one has DVD's one is willing to lend
out, and for keeping track of DVD's that have been lent.
Additionally, preferred embodiments have functionality for rating
lenders of DVD's according to their reliability (much as is done on
eBay or various action sites with respect to sellers). Skilled
practicioners of the art of Web programming will immediately see
how to create appropriate user interfaces.
[0216] In some embodiments this lending data is stored on the
server for easy access by various clients and in others it is made
available by peer-to-peer means.
[0217] The idea is that when the system finds people who have
similar tastes, they will be able to help each other by lending
DVD's to each other. Because they have similar tastes, they will be
able to lend multiple DVD's. They may also email each other or chat
with each other about DVD's of interest through addresses made
available through the interface or through automatic means. These
factors lead to a relationship of trust, which minimizes the risk
in sharing DVD's. So such a service has the potential to do what
netflix does, but since there is no central repository of DVD's, at
much lower cost.
[0218] Of course other physical objects of interest than DVD's are
the subject of other embodiments; CD's is one applicable subject
area.
APPENDIX 3
Introduction (Appendix 3)
[0219] This appendix describes another way of implementing key
functionality of the invention, including but not limited to
facilitating retrieval of representations of nearest neighbor
candidate taste profiles and associated user identifiers in an
order such that said nearest neighbor candidate taste profiles tend
to be at least as similar to a taste profile of the target user
according to a predetermined similarity metric as are subsequently
retrieved ones of said nearest neighbor candidate taste
profiles.
[0220] The representations mentioned in the previous paragraph may
be the user profiles themselves (including the taste profiles), or
just the taste profiles (which should include an identifier of the
user)--or they may be user ID's of the users, or URL's enabling the
data to be located on the network, or any other data that allows
taste profiles and associated user ID's to be accessed. These are
all functionally equivalent from the standpoint of the
invention.
[0221] So that it may be taken separately, this Appendix describes
the invention anew. The present invention is a new approach to
dynamically creating online groups of similarly-minded people for
both community-building and generating recommendations of items of
interest to the communities.
[0222] The invention is a form of distributed computing for
searching which we will refer to as "distributed profile climbing"
or "DPC". In preferred embodiments it is a kind of middle ground
between a server-based Internet service and a peer-to-peer one.
[0223] The invention consists of a networked computer system
running special software. The network is typically the Internet
(but can be any network which interconnects computers) and the
computer can be a broad range of computer hardware that a user
might own, a typical personal computer running with 256 megabytes
of RAM a Pentium processor being one example. The connection to the
network may be a direct connection, or may be wireless, based on
radio, light, Ethernet cabling, etc.
Distributed Profile Climbing
[0224] Peer-to-peer networks are a popular way to handle such
challenges as sharing files between many users. The main problem is
that not everyone who wants to participate in such a network can do
so fully. This is for a number of reasons--computers may not be on
all the time, or they may be portable, or they may have firewall
and/or network address translation issues.
[0225] Pseudo-peer-to-peer networks handle that problem by creating
proxies for the machines of each user who wants to participate.
These proxies exist on server systems, but typically the technical
requirements for those servers are light because the proxies merely
store and transmit data related to the machine they are
proxying.
[0226] An example of this is Radio UserLand's "upstreaming". Radio
UserLand is a software package that runs on end-user computers and
lets users create weblog entries. Those entries may then be sent
("upstreamed") to UserLand's servers. Web users who wish to view a
Radio UserLand customer's weblog can then look at the proxy data on
UserLand's servers. Note that, in a world where everyone had
computers always able to allow access to other users, there would
be no need for this upstreaming to take place. Each weblog writer's
machine could serve their weblogs to the rest of the world. But we
are not in such a world, so the practical solution is to send the
weblog data somewhere where can be always available to other
people, in the form of a data object which is located at a
particular URL on a reliable server. This data object is the proxy
for the user's machine.
[0227] DPC networks share a common foundation with
pseudo-peer-to-peer networks like UserLand Radio in the sense that
each user's data is represented by a proxy data object located on a
remote server. However, in DPC networks, this data contains a
profile of the user in order to compare similarity of interests. In
preferred embodiments, the proxy object for a user further contains
key information for other users who have already been found to be
similar in interests to that user. This key information is
sufficient to enable the proxies of those other users to be
accessed (typically, this would be by means of constructing a URL
that accesses the proxies).
[0228] One very important aspect of searching for similar profiles
is intelligently handling users that have already been compared at
least once. In some cases, it may be desired to never compare them
again; in others it may be desired to compare them again after a
certain amount of time or a certain number of updates have
occurred. Most approaches for taking care of this involve storing
representations of which pairs of profiles have already been
compared.
[0229] For instance some solutions store a table with a
concatenated key containing the logon ID's of the two users that
have been compared. But this is a problem. If we assume that over
time every user will be compared to every other (ignoring the
expense of those comparisons for now) and there are 10,000,000
users in the database, the result is a table with
100,000,000,000,000 records. That is not within the realm of
reasonable possibility for affordable server installations.
[0230] However, now assume there are 10,000,000 users each with
their own machine, and each machine stores the logon ID's
approximately the approximately 10,000,000 users it may have been
compared to over time. This is entirely within reason given the
most computers being sold today are equipped with 10's of gigabytes
of storage. This is the way DPC handles the problem, in embodiments
which involve such lists. Preferred such embodiments contain the
calculated similarity metric for each comparison as well as the
date and time of the comparison, and other pertinent information
may be included as well.
[0231] Moreover for embodiments that handle previously-checked
lists, there is no need for the kind of very sophisticated, highly
scalable database software that would be required to store that
data on a central server.
[0232] Furthermore, in most DPC systems, the similarity metrics are
computed on the user's machines rather than on the server. This is
not a requirement, but it does help to distribute the workload and
simplify the scalability issues for the server.
[0233] As a matter of practical implementation, preferred
embodiments where there are large numbers of users divide the
proxies for various users among separate servers residing in one or
more physical hosting sites. Usually the proxies are divided up in
such a way that a hash function based on the user's ID can be used
to determine which server (or subgroup of servers) hosts that
user's proxy. The benefit of dividing the server side up this way
is one of simplicity and cost--there is no need for a
high-performance central database system. Instead the servers can
operate in relative isolation to each other, even storing all data
in local RAM for speed, using communicating with other server
hardware for control and backup purposes.
[0234] An algorithm for one embodiment of the invention is shown
below. Steps are carried out in the order shown. Deeper indentation
is used in the representation of repeated groups of operations, or
operations that are dependent on the result of an "if" test. An
"else" relates to the previous "if" at the same indentation level.
A "break" causes the process to immediately terminate the currently
innermost loop, while allowing outer loops to continue undisturbed.
The operations depicted carried by the software operating on
end-user machines, except that the server is invoked to provide
data on occasion.
[0235] First we will introduce some terms. THISUSER is the user
whose machine the algorithm is running on. Each user has an
associated NEIGHBORBAG which is his current list of ID's of similar
users. In this example embodiment, the NEIGHBORBAG has a fixed
maximum size. PREVIOUSLYCHECKEDBAG is collection of users that have
already been checked as potential neighbors (members of
NEIGHBORBAG).
[0236] In the example which will follow, all similarities are
between 0 and 1, and higher similarities are better. When
similarities between THISUSER and another are considered, it is
implied that one of the following happens: a) the user's machine
requests that the server send the other's user's taste profile,
such as an encoded version of the relevant data from his iTunes
Music Library database, and the taste profiles of the two users are
compared on THISUSER's machine, or b) the server compares the two
users using that same data and returns the result to THISUSER's
machine. The former has the overhead that the profiles need to be
sent to the user's machine, which consumes network bandwidth. The
latter adds more work that must be done on the server side,
increasing the complexity of the server. Different embodiments need
to trade off these factors.
TABLE-US-00002 repeat as long as THISUSER is online: ask the server
for the ID of a random, already-existing user; set N to be this
returned ID set PREVCLIMBER to null; set PREVSIMILARITY to 0
repeat: if N is a member of THISUSER's PREVIOUSLYCHECKEDBAG, and
was added to it < 6 months ago: break ask the server for N's
NEIGHBORBAG; save it in CLIMBERBAG set C to be the member of
CLIMBERBAG that is most similar to THISUSER add all members of
CLIMBERBAG that are not already there to THISUSER's
PREVIOUSLYCHECKEDBAG set CSIMILARITY TO C's similarity to THISUSER
if CSIMILARITY > PREVSIMILARITY: set PREVSIMILARITY to
CSIMILARITY set PREVCLIMBER to C set N to C else: if there are any
members of THISUSER's NEIGHBORBAG that have a similarity to
THISUSER that is < PREVSIMILARITY: If the maximum size for
NEIGHBORBAG has been reached: remove the member of THISUSER's
NEIGHBORBAG which has the least similarity to THISUSER add
PREVCLIMBER to THISUSER's NEIGHBORBAG break
[0237] Note that this invention must not be construed as being
limited to the algorithm above, which is presented merely as one of
the more simple ways of implementing the invention.
[0238] However, all approaches that fall within the scope of the
invention have in common that profiles arrive at the client node in
an order that tends to receive the profiles most similar to the
current user first. Accordingly processing is included above
whereby, a profile isn't retrieved again until a sufficient time
period has passed for the profile to have appreciably changed. In
the short term, the most similar matches will exhaust themselves
and less similar matches will follow.
[0239] At the beginning the retrieved profiles are essentially
random, but the process quickly "climbs" to strong matches. The
process therefore will not retrieve profiles in exactly the ideal
order; however if the techniques used do not generally retrieve the
profiles in exactly the ideal order, this method will retrieve
profiles in a good enough order that once climbing has reached a
high level of similarity and profiles are not being retrieved
because they already have been, we have the required general
decreasing similarity.
[0240] The climbing is accomplished by means of calculating the
similarity metric with respect to the nearest neighbors of a user
for which the similarity has previously been calculated, where the
latter was found to be at a level high enough that it is worth the
expense of going on to retrieve the interest profiles for that
user's neighbors to determine whether one or more of them will have
an even greater similarity to the target user.
[0241] Some peer-to-peer networks, such as the Morpheus
file-sharing network, have an architecture which causes data which
would traditionally be stored on a server to instead be stored on a
subset of user computers. We will refer to such servers, in the
context of this invention (not necessarily in the Morpheus context)
as user-associated servers. In the conduct of the illegal file
trading of copyrighted files, the main "advantage" of this
technique is arguably that there is no company which controls the
master index and which can therefore be prosecuted or sued.
[0242] However, from the point of view of the present invention,
there is another reason, and that is to completely (or almost
completely) eliminate the expense associated with a central server.
If there is a central server (or server network separate from
user-associated servers), then some entity has to pay for
maintaining it, providing the bandwidth, etc. Without one, that
necessity disappears. Eliminating that necessity enables this
invention to be embodied, in a sense, in "pure software" such as an
open-source software project, instead of needing to embody it in a
project run as a business in order to pay for the servers. Based on
the experience of the file-sharing networks, there are enough users
who do not have severe firewall or connectivity issues and who are
willing to help others by making their resources available that
this is a feasible solution. Moreover, unlike file sharing
networks, there is little real problem if a user-associated server
becomes temporarily or permanently unavailable, because the
searching is normally done in the background rather than in
real-time.
[0243] Note that this specification has already described how a
hash of the user's ID can be used to determine which server to
access for his data. In order to extend that to using
user-associated servers, more is required (and the
already-described hash may or may not be part of that).
[0244] In one set of embodiments there is still a central server
but rather than serving the taste profiles, it contains a list of
identifiers which can be used to construct the URL's where the
taste profile for each user may be found. So the actual amount of
data that needs to be stored on, and sent from, the server is far
less than in the earlier description. For many implementations, the
load will be light enough that a single desktop computer with cable
modem or DSL (or similar) connection to the Internet will be
enough.
[0245] The Gnutella network, for example, provides a "cloud" of
user-associated servers, many or all of which store the URL's (or
data that can be used to construct the URL's) of many or all of the
other user-associated servers. When a user obtains
Gnutella-compliant software (whether by download or by other means)
it normally is distributed with a list of user-associated servers
that are frequently available. The software then contacts those
servers, and can get lists from them of other such servers. The
local node is then updated with this information, and it is
available to other nodes that might eventually contact this node.
Thus, no single central server is required.
[0246] This specification will not describe the construction of
such networks in detail; rather the technical descriptions for
Gnutella and other such networks, readily found online using such
search tools as Google, should be used. Use such existing networks
as a model for constructing a "cloud" of nodes which point to each
other and obviate the need for a central server.
[0247] Preferred embodiments of the invention where the profile
data is stored on user-associated servers generally use the same
computers for storing that data as are used by their associated
users as their day-to-day computers, with the exception that they
must be accessible to inbound connections (i.e., few if any
Firewall or NAT issues should apply, and they should be connected
to the Internet, and turned on, a substantial amount of the time).
Each user-associated server stores the profiles and neighbor lists
of a number of other users.
[0248] For preferred such embodiments, the step of retrieving a
random user ID is modified so that instead of asking a central
server, first a random user-associated server in the cloud (or
semi-random, influenced by the fact that only a subset of the cloud
may be known to the node at the time) is chosen, and then that
server is asked to provide a random user ID of those whose profiles
and neighbor lists are stored on that computer. Then the algorithm
proceeds as before, with the exception that instead of retrieving
just the ID of other users, enough data is retrieved to construct a
URL where that user's information is available. Then it is accessed
at that location. Further, if an access fails because the URL
doesn't respond or the data that is supposed to be there isn't, a
"break" is executed and the innermost loop explicitly spelled out
in the pseudocode is exited.
[0249] Further embodiments lower the percentage of times
non-response or not-found errors occur by providing multiple URL's
where the same data can be found on different user-associated
servers. Then if one fails, one or more fallback machines can be
tried.
[0250] In preferred embodiments, user-associated servers take
responsibility for serving the nearest neighbors of that particular
user to the broader community. This causes data for similar users
to be gravitate toward being stored on the same machines. One
advantage of this technique is that if user-associated server A is
being accessed and provides a NEIGHBORBAG for similarity testing,
it is likely that when the accessing node wants to get the taste
profiles for the users in the bag, seconds or minutes later, that
machine will still be available on the network.
[0251] A further improvement is that, instead of sending the taste
profiles for the accessing user for the similarities to be
calculated, they can be calculated on the user-associated server in
cases where it is judged that it would be more efficient when data
transmission expenses are calculated, to send the data there. In
such a case, the querying node would upload its taste profile to
the user-associated server so that multiple comparisons can be
carried out there without further need for network data
transmission.
[0252] In further embodiments, such user-associated servers not
only store the neighbors of their associated users, but also other
neighbors with relatively high similarity to other users that are
stored on that user-associated server. For instance in some
embodiments a centroid may be calculated that represents an average
of the taste profiles of the users stored on that server. One type
of taste profile contains identifiers for every song a user has
played on a particular target platform (such as Apple's iTunes),
together with the date it was first added to the user's collection
and the number of times he has played it. A centroid averaging a
number of such user profiles might contain the identifiers for all
the songs played by any of the associated users, together with, for
each song, the average of the dates it was added to the system and
the average number of plays of that song per user.
[0253] The algorithm described above to find the most similar
neighbors for a user may be carried out but with respect to this
centroid rather than with respect to the user. The ID's of the
users most similar to this centroid are stored in a neighbor list
for the centroid, and their profiles and neighbor lists (together,
their proxies) are the ones that that particular user-associated
server takes responsibility for serving to the community. But it
should not be construed that the invention is limited in scope to
the concept or "centroid" or "averaging." Any summary of multiple
user's profile information that is comparable via a similarity
metric to an individual user's profile is equivalent for the
purposes of the invention.
[0254] For example, in some embodiments that involve user's
interests with respect to text documents, a user's interests may be
captured in a list of the most unusual keywords that regularly turn
up in text they read. For instance a paleontologist might read text
containing the word "archaeopteryx" fairly frequently. The exact
frequency isn't as important as the fact that the population at
large very rarely reads text with that word whereas the
paleontologist frequently does. So, the paleontologist's interest
profile can be realistically represented by a list of such words
that meet certain predetermined thresholds for "unusualness" with
respect to the general population, and "frequency" with respect to
the user himself. Extending that concept to a group of users rather
than a single user, it is clear that the interests of a group of
similarly-minded individuals can be represented by a list that
contains all the words that are in any of the individuals' personal
word-lists (or that are in some predetermined proportion of such
lists). This is a completely different approach from using
averaging to create a centroid, but it falls equally within the
scope of the invention, as do all other approaches which serve the
purpose of representing an individual's interest where individuals
are concerned, and summarizing such interests for a group where
groups are concerned, as long as it is possible to compare the
interest profiles of individuals to each other or individual
interest profiles to summary interest profiles or summary interest
profiles to summary interest profiles and calculate appropriate
similarity metrics. (With respect to the word list, a simple
similarity metric is to calculate the percentage of words out of
the total pool of words formed when the lists are combined are held
in common. A more sophisticated approach is to consider every word
in the combined list to be a "trial", with success being that the
word is held in common; the similarity metric is then the posterior
mean based on a binomial distribution and a beta prior.) Note that
this process may frequently result in more than one user-associated
server hosting the proxy of a given user. That is good, because
that allows for redundancy in the system for times when a
user-associated server is not available. Moreover, there is more
redundancy for users who are similar to a lot of users then for
users who are similar to only a few others. This allows for
providing the most reliable and efficient service to the most
people.
[0255] As a further example, in some embodiments the summary is
simply the taste profile of the user associated with the
user-associated server that is directing the search. By finding
nearest neighbors to that such a user is also finding neighbors who
are relatively similar in taste to other users whose profile is
stored on that user-associated server, as long as the question of
whose profile shall be stored is also resolved by virtue of having
a high similarity metric with respect to the user associated with
the user-associated server.
[0256] In further embodiments, each user-associated server carries
out searches using an algorithm almost identical to one of those
described above, with the exception that the search is done with
respect to similarity to the collection of users whose proxies
(whether the proxy contains the taste profile or the user's
neighbor list or both and/or contains other items) are already
being served from that particular user-associated server. (This is
as opposed to doing such searches with respect to each individual
user whose proxy is stored on the server or facilitating, by
serving data, such searches carried out by the individual
user-associated nodes.) This may be done, as described above, by
comparing other users to a centroid of the collection or it may be
done by other summary means (all of which fall within the scope of
the invention). The standard literature on the subject of data
clustering will reveal a number of methods that are equivalent for
the purposes of this specification. In preferred such embodiments,
the user who is associated with the user-associated server is
always among the users whose proxy would be added to that
collection if the user wasn't already there. For instance, in the
method which involves a centroid produced by averaging the profiles
of the users, the algorithm would never remove the user associated
with the user-associated server from the list of users whose
profiles are averaged to produce the centroid.
Notes for Appendix 3
[0257] A central server may be not only a single server computer,
but a set of such computers, the distinguishing characteristic not
being the number of computers in the central server, but rather the
fact that they are not associated with a particular user but rather
made available on the network to serve data to a substantial number
of user-associated computers.
[0258] When this specification uses the term "associated with" for
the relationship between a user and a computer, the computer is the
computer that the user normally accesses to get the benefits of the
system, for instance, viewing a list of the users that are more
similar to him than any others that have been examined.
[0259] The term "target user" is used occasionally in this
specification to refer to a particular user who is using the
invention and for whom the invention has found, and/or is finding,
other users with similar interests and/or tastes.
[0260] Preferred embodiments make a display of the individual users
who have been found to be most similar to the target user available
through a computer user interface. In some embodiments this takes
the form of a list; in others there are other displays such as
images representing the users in 2D or N-Dimensional space. In some
embodiments the positions such images take with respect to each
other in the visual plane represent how similar they are to each
other.
[0261] Preferred embodiments make recommendations to the target
user of specific items based on a list of nearest neighbors, that
is, a list of neighbors who are relatively similar to the target
user in taste when with respect to other users of the system. They
do this by processing the preferences of the nearest neighbors in
ways that are similar to how this is done in other
nearest-neighbor-based collaborative filtering systems such as, for
example, in the GroupLens Usenet filtering system,
http://www.si.umich.edu/.about.presnick/papers/cscw94/GroupLens.htm,
incorporated herein by reference, or the system described in
Upendra Shardanand's 1995 thesis, Social Information Filtering:
Algorithms for Automating "Word of Mouth,"
http://citeseer.nj.nec.com/rd/61053528%2C323706%2C1%2C0.25%2CDownload/htt-
p://citeseer.nj.nec.com/cache/papers/cs/15862/http:zSzzSzmas.cs.umass.eduz-
Sz%7Ease
ltinezSz791SzSzshardanand.social_information_filtering.pdf/sharda-
nand95social.pd f, incorporated herein by reference. Note that
those two papers, and others, describe how recommendations may be
made once a list of nearest neighbors has been determined, and
those and other approaches exemplified by those may be used once
such a list has been determined, regardless of the particular
calculation originally done to determine the degree of similarity
another user has and thus how the decision was made about how to
add him to the list of nearest neighbors.
[0262] However, it is important to note that while the papers
mentioned above make recommendations based on ratings manually
entered by the users, the present invention may be used in
situations where no such ratings are available. Instead other
information may be available, such as the fact that the user has
purchased particular items, or has chosen to experience them a
certain number of times (for instance, has played a musical track a
certain number of times). When only purchase data is available, a
purchase can be considered to be equivalent to a rating of "good"
and no purchase can be considered equivalent to a rating of "poor".
When the number of times a user has chosen to experience an item is
available, an easy way to approximate the effect of having ratings
is to rank the items by the number of experiences. Then divide the
rank by the number of items. This results in a number between 0 and
1 that can be used as a rating-equivalent, normalized to that
interval so that the "ratings" of all users are on the same scale.
So the techniques mentioned in the afore-mentioned papers, and
others, are still usable even where there are no explicit ratings.
However, for purposes of example, a particular technique of making
recommendations for situations where nearest neighbors have been
found and "number of experiences" data is available for each item
will be presented here.
[0263] This technique is to simply add up the number of experiences
for each item for all nearest neighbors. For example, assume that
out of a universe of 1,000,000 music fans, the system has found 100
nearest neighbors for the target user. For each item associated
with each fan, there is a count of how many times each song has
been played. If the system simply adds up these counts for each
item, the item with the highest total count may be considered to be
the most popular item in that community, and should be recommended
to the target user if he hasn't already experienced it.
Equivalently, one can compute the arithmetic mean of the number of
plays, where the number of plays is 0 for users that haven't
experienced the item at all.
[0264] A variant of the approach described in the previous
paragraph that is arguably more reliable is to compute log(1+K) for
each neighbor/item combination, where K is the number of times the
user has experienced the item in question, and then calculate the
sum of these values for the population of nearest neighbors. The
higher that sum is, the more highly the item should be recommended.
The advantage of using the log is that for an item to be
recommended highly, it is more important for the item to be
experienced often by a large number of nearest neighbors than it is
for a few nearest neighbors to experienced the item a huge number
of times.
[0265] The same two papers as mentioned above that discuss
collaborative filtering, and others such as the specification of my
own U.S. Pat. No. 5,884,282, herein incorporated by reference,
describe different ways of creating metrics to capture degrees of
similar between two users. All such metrics fall within the scope
of the invention. The invention isn't limited to particular
metrics; rather the focus of the invention is on the structure of
the search and where the relevant data is stored.
[0266] A similarity metric that is used in preferred embodiments
where explicit user-entered ratings are not available is the
following. Assume user A is the target user, and we want to know
how similar user B is to user A. We calculate an approximation,
subject to certain assumptions which are useful to us but may not
be true in the real world, of a certain probability. This can be
loosely summarized as being probability that, if a randomly chosen
item X not in A's collection but in B's collection is put into A's
collection, that if we pick a random time in the future when A is
experiencing an item from his collection, it will be X. An
implementation of this concept that teaches the technique is
included in the tasteprofile.py module included the computer
program listing appendix and described in Appendix 4.
[0267] Embodiments of this invention serve the useful purpose of
determining which other participating users are most similar to a
user who is a participant in the system, and storing that
information in the computer for purposes of displaying that
community and/or making recommendations of desirable items. Further
embodiments not only store that information, but display the
community members and/or recommendations through the system's user
interface.
[0268] Some embodiments store each user's profile on their
associated computers. Due to issues mentioned above, many
user-associated computers may not be accessible to other users from
the internet. So a technique must be provided by which users can
serve their profiles when they are stored on user machines.
Gnutella-style networks provide an example for this. Nodes which
are accessible from the Internet allow incoming connections to be
made from nodes which are not necessarily connected. Then, data on
those not-otherwise-accessible nodes is made available to other
nodes on the network, through the network-accessible nodes which
the not-otherwise-accessible nodes are connected to. In the case of
Gnutella, this data includes lists of available files and the files
themselves. (See
http://www9.limewire.com/developer/gnutella_protocol.sub.--0.4.pdf,
hereby incorporated by reference, for more information on the
details of the Gnutella approach.) In the present invention, the
network-accessible servers usually store lists of the user ID's
associated with the nodes they are connected to, and when a request
arrives for data associated one of those ID's, the request is
routed to the appropriate connected node, the data is retrieved by
the network-accessible node, and then sent by the
networked-accessible node to the requesting node. Most embodiments
that use the search algorithm described earlier in this
specification modify it when it is used in the configuration
described in this paragraph so that if the data for an ID is not
available a "continue" is called in the innermost loop so that
control goes to the top of the loop, and processing continues as if
that information had not been requested. Note that to facilitate
"hits" occurring as frequently as possible, nodes normally try to
connect to network-accessible computers who are on their
nearest-neighbors list. This makes it likely that
network-addressable nodes will be connected to some of their
associated users's nearest neighbors, so that when the interest
profiles of neighbors are needed by the algorithm, they can more
often be retrieved. In general, the presented algorithm is modified
so that where, originally, ID's of similar users are requested,
information is provided that can be used to construct one or more
URL's where the information can be found. If the information is not
found on a directly network-accessible computer, the URL of a
network-accessible one (such as the one providing the URL) can be
given, which includes parameters such as the ID of the user whose
information is desired, to tell that node which possibly-connected
node to get the information from. An individual of ordinary skill
in the art of peer-to-peer software development will understand how
to create the necessary software in accordance with this
description. It should be stressed that this paragraph is for
example only, and that there are many equivalent variants that
involve, for instance, caching data on intermediate user-associated
nodes, transporting profiles to other nodes for comparison, etc.
This invention's scope must not be construed as being dependent on
specific techniques for making the data and computations available
in a peer-to-peer setting.
[0269] In some embodiments two forms of interest profiles are
created and stored. One is a very small (in terms of the amount of
data) representation. For example, if the main interest profile
contains the song names, and artist names for songs in the user's
collection and the number of times he has played each one, which
could have thousands of entries, this miniature profile may contain
only the user's most frequently-played 10 songs identified by a
hash such as that generated by Python's built-in hash( ) function.
Preliminary screening, including climbing, happens as described
elsewhere in this specification using the miniature rather than the
full profile. Then as a last step, before adding another user to
the target user's nearest neighbor list, the full profiles are
checked to be sure the similarity metric is really high enough that
the user should be a nearest neighbor (for instance, that it's
higher than the metric associated with the least similar neighbor).
If it doesn't meet this final test, it doesn't go on the list.
[0270] When a miniature profile is used, any technique that serves
to produce a relatively small (from the perspective of
number-of-bytes), not necessarily complete, representation of the
data in the interest profile may be used. The scope of the
invention is not limited to particular miniaturizing technologies.
For instance, in addition to the simple approach described above,
applicable approaches include using all of the item hashes without
any counts, using a random selection of items and including the
song name itself rather than a hash and optionally further using
standard compression algorithms such as are in the standard Python
zlib library.
[0271] "Neighbors," "users," and similar terms are often used in
this specification to represent their interest profiles, ID's etc.;
the meaning is clear in the context.
APPENDIX 4
Source Code
[0272] The source code is contained on the computer program listing
appendix. Notes about several specific modules follow:
[0273] MODULE: tasteprofileclass.py
[0274] The pair of classes appearing in this module, CalcData and
TasteProfile, are tightly connected. Each TasteProfile object may
have a number of associated CalcData objects. The CalcData objects
represent one song in the collection of the user whose TasteProfile
it is.
[0275] Methods are provided for loading the object from various
sources; a programmer of ordinary skill will readily infer the
formats from the input code.
[0276] It is worth noting that for convenience and to save memory,
songs are frequently identified by an MD5 hash based on combining
and normalizing their artist, album, and song names.
[0277] The most important method is probably
TasteProfile.calculateSimilarity( ), which compares the current
called TasteProfile object with another one passed to it as a
parameter. Usually this is used for the local user to sequentially
compare his profile to those of other users, in order to find the
best ones--the nearest neighbors.
[0278] In such usage, a nearest neighbor list is maintained of a
predetermined length is maintained, and when a profile of greater
similarity to the local user comes along, compared to the least
similar of the current nearest neighbors, the least similar one is
removed from the list and the new one added.
[0279] MODULE: recommenderclass.py
[0280] This module handles the task of using the list of nearest
neighbors, and their associated profiles for recommendation
purposes.
[0281] It makes recommendations, subject to an "adventurousness
control." When the control is at one extreme, it looks for
consensus among neighbors; as it moves toward the other extreme, it
is more and more sensitive to opinions of individual users. (In the
current embodiment, these opinions are expressed passively simply
by recording how many times each song is played.)
[0282] MODULE: genrerankhandlerclass.py
[0283] The code in this module represents one way of clustering
cluster data containing songs where the songs (or most of the
songs) have associated genre information. Of course, it can be used
analogously for other subject areas; for instance in the area of
academic research, it could make use of the papers in the users'
collections (rather than songs), and their associated keywords
(rather than genres).
[0284] This algorithm has the advantage that it is much faster than
most general clustering algorithms, due to making use of the effort
that originally went into creating the genre information.
Furthermore, programmers of ordinary skill in the art will readily
see various ways of improving the speed of the code further (at the
cost of more code complexity).
[0285] MODULE: clusterfitterclass.py
[0286] On a server, this is a helper class for
genrerankhandlerclass.py. However, it has another use as well. On
the client, it serves to tell the clients which identifier is
associated with the cluster a client should download first. That
is, it outputs a sorted list of clusters with the ones most likely
to yield high similarity to the local user.
[0287] It does that by means of summary data (the xInitData
parameter on the init method) that is sent to the client from the
server which contains data that summarizes the differences between
the clusters.
[0288] In the current embodiment (from which this code is derived),
this enables clients to request the clusters that are most likely
to have good similarity matches first; this downloading is
accomplished via BitTorrent. We do not include the
BitTorrent-related code here because techniques for accomplishing a
BitTorrent download are readily apparent to a programmer of
ordinary skill.
APPENDIX 5
[0289] This Appendix describes a class of embodiments wherein some
of the user nodes run software that has only a one-way connection
to the other nodes and server (if one exists). These embodiments
include cases where the connections to the other nodes and server
(if one exists) involve more than one medium. We will focus on a
specific example where some of the user nodes, which may be full
personal computers or may be hand-held devices such as Apple
Computer's iPod, have radio circuitry incorporated into them which
allow them to receive transmissions from terrestrial or satellite
radio broadcasters. (In the case of satellite transmitters, these
may include the specific hardware associated with the Sirius or XM
satellite radio services.)
[0290] In the prior art the time of this writing, Sirius Satellite
Radio has announced a handheld device, to be called the S50, which
will work with its satellite network and save songs on its internal
data storage. It does not have the ability to receive satellite
signals on its own. Rather it can only receive songs when attached
to a docking device. Samsung has announced its neXus XM Satellite
Radio/MP3 Players. Users will be able to "tag" songs they hear on
the radio for purchase through the XM+Napster online service. The
neXus unit will not have a built-in antenna; rather it will connect
to a dock which has an antenna, and will record songs from the
satellite service for later play without the dock attached.
[0291] XM Satellite Radio sells a Delphi XM SKYFi2 unit which
includes internal storage for pause and 30-minute replay, although
the antenna is separate. It has announced a Delphi XM MyFi unit
which is handled and includes an internal antenna.
[0292] What is missing from the prior art is a way to enable the
user to receive personalized recommendations or a "virtual channel"
constructed automatically for the benefit of that user to enable
him to have the experience of a radio channel specifically geared
towards his or her individual tastes.
[0293] The present invention provides a solution to this need.
[0294] In this set of embodiments, the nodes with two-way
connections work as described elsewhere in this specification. On
the local node, reference data is collected, nearest neighbors
found, recommendations are generated, and the taste profile of the
local user is distributed to other user nodes to be used by them in
a similar way if they are deemed by the software to be similar
enough in represented taste and interests to those local users. Not
all embodiments of this variant that fall within the scope have the
nodes with two-way connections receiving the taste profiles in an
order related to likely similarity to the local user's tastes.
Typically these nodes are connected by a network such as the
Internet which readily handles two-way communication.
[0295] The nodes with one-way connections, in preferred
embodiments, receive taste profiles via satellite radio. Satellite
radio uses digital signals that can easily send taste profile data
on one or more channels while sending audio and/or video content
such as podcasts on others, and/or it can send a subset of those
types of data on a single channel by transmitting one type at one
time and other types at other times.
[0296] In preferred such embodiments the one-way nodes, which in
further embodiments may be one-way at some times and two-way at
other times, are hand-held devices like the Apple iPod which
include a CPU and memory to store content data such as audio and
video data, where such memory will include RAM and may include hard
drives, flash memory, or other kinds of persistent storage.
Hand-held devices are meant to be carried from place to place by an
individual, and many such devices do not have ongoing two-way
communication abilities due to the difficulties and expense of
maintaining network connections from remote locations. For such
devices, satellite radio provides an excellent transmission medium
for the taste profile and digital content information used by the
present invention.
[0297] The one-way devices (which may, in some embodiments, have
two way connections at other times), receive taste profile and
content information. They also have at least one way of inferring
the user's tastes and interests. In various embodiments these may
include buttons to rate content he is hearing and/or viewing, or
they may include monitoring which content the user stops
prematurely or skips over using a mechanism such as a fast-forward
button, and which content the user repeats. Some embodiments
monitor whether a user uses a rewind-like button to experience
portions of content more than once; for instance in a listening to
spoken word content, the user may want to hear some of it more than
once to aid his understanding. Preferred embodiments have an input
mechanism such as a button that indicates that a user likes a unit
of content (such as a song) and would like to hear it again.
[0298] By using such mechanisms, input is provided to the software
whereby the software creates a profile indicating certain likes
and/or dislikes of the user.
[0299] Taste profile data received via the one-way medium is then
processed as described elsewhere in this specification. Taste
profiles that are similar to those of the local user are stored and
used for recommendation purposes. User profile information may also
be used for community purposes; for instance, in a cell phone
embodiment, a telephone number or address may be provided whereby
the local user can call the other user whose taste profile matched.
In some such embodiments, additionally, a contact recipient will
receive bio and/or taste profile information from the local user
and hear or view it before deciding whether to take the call; in
further such embodiments the receiver has criteria set in his
software that automatically screen for certain biographical
characteristics or a certain degree of similarity before the user
is alerted to the incoming call. In further such embodiments
location data such as GPS information is used, so that the local
user is made aware of the location (which may not be current) of
the remote user, or the software screens on location data so that
the local user is only alerted to profiles associated with nearby
locations, and/or, alternatively, the remote user's software
screens attempted contacts based on the location of the local
user.
[0300] Note that preferred such devices have both satellite
radio-receiving and cell phone capabilities. Satellite radio
reception may be maintained with typically lower consumption of
bandwidth and energy resources than cell phone connections, and
typically have higher data transfer rates, so it is helpful to
receive a stream of data from the satellite, while also having the
hardware required to allow the user to make a cell phone call.
[0301] A key to this class of embodiments is the fact that the
overall network contains both one-way and two-way nodes at a given
instant in time (again, some of these nodes may change roles at
other times). This enables taste profile and (in preferred
embodiments) biographical information or other data such as
location to be sent on the network to be received and used by the
one-way, receive-only nodes. Because of this mix of node types, it
is practical to collect the taste profile data on the two-way
nodes, which is used to make recommendations on the one-way
nodes.
[0302] In preferred embodiments of two-way nodes containing a
broadcast (for instance, satellite) radio receiver as well as
wi-fi, ethernet, or other connection to a typical Internet service
such as a dial-up service, cable modem, or DSL, data derived from
other users is substantially or wholly received via the broadcast
radio receiving circuitry, while data is uploaded via the Internet.
This minimizes the use of limited Internet "bandwidth" for
receiving large amounts of data.
[0303] A detailed description of a particular embodiment:
[0304] Software incorporating all or much of the software contained
in this specification runs on a large number of desktop personal
computers, connected to the Internet. We will refer to it as the
Goombah software, since there is presently software of that name
that incorporates much of that code. The users of those computers
use Apple Computer's iTunes software to play music. iTunes writes
an XML file on disk containing the identifiers for each track in
the user's collection. The Goombah software reads this XML file and
uses it as the user's taste profile data. This data is sent to a
server, under the control of which the data is communicated, not
only to other personal computers, but to a terrestrial radio
transmitter that sends the data to the satellite or satellites
being used to facilitate a satellite radio service such as XM or
Sirius. From the satellites it is broadcast to portable units which
could be, for instance, Sirius or XM-enabled versions of Apple
Computers iPod device.
[0305] On the other personal computers, the taste profiles
contained in the data play the role of candidate nearest neighbors;
the nearest neighbors are selected and used to provide content
recommendations, as described elsewhere in this specification.
[0306] On the portable devices, an analogous process of neighbor
selection and recommendation occurs. However in the embodiment
currently being described, the recommended music takes the form of
at least one virtual channel. That is, from the user's point of
view, it behaves much like a standard satellite radio channel, but
at least much of the time, the content is selected, scheduled, and
played on the user's local portable device.
[0307] In this embodiment, there is an easily accessible Save
button. When the user first starts using the device, he tunes into
one of the standard satellite channels which he thinks is likely to
be a good approximation to his tastes. When he hears content (for
instance, a song), that he particularly likes, he presses Save. (In
some other embodiments, there is a button for the explicit purpose
of enabling the user to indicate that he likes a song; there may be
another one to indicate dislike; or there may be an input mechanism
such as physical "radio buttons," which allow only one to be
pressed at a time, allowing a degree of liking a song to be
expressed; other variants are also applicable. In further
embodiments there is not a dedicated physical button for this
purpose, but instead controls are provided whereby the user can
navigate through a menuing system to choose a "Save" option.
Samsung's neXus player will have a mechanism through which the user
can "tag" a song for purchase; since available photographs do not
show a dedicated button for this purpose but rather an input
mechanism that appears similar to the iPod's for navigating a menu
system. The tagging function is undoubtedly activated through that
menu system. In the context of the neXus device, tagging a song
implies the user probably likes it because most people will tend to
buy songs that they like [although some will buy songs for others
such as their children; still the statistical likelihood that
tagging implies liking a song makes it appropriate for our
purposes]. So embodiments built into an improved neXus device may
use the tag function for this purpose. Alternatively or in
addition, a separate "I like this" option may be available through
the same menu structure which would serve the purpose here
attributed to the Save button. Ideally, such a future device will
have a built-in antenna akin to the Delphi XM Myfi's antenna. All
such variants fall within the scope of the present invention.)
[0308] The song has been stored in RAM even after the earlier parts
of the song were played. So it is in RAM and available to be moved
to persistent storage such as flash memory or a hard drive when the
Save button is pressed. Typically there is a pause between songs,
and pressing Save during that pause causes the previously played
song to be saved. When a song is Saved, it can be played again
later with greater frequency than would be the case if the user
simply waited for the satellite channel to broadcast it again. The
portable device automatically schedules the song to be played again
later, and does the same for other Saved songs. For instance Saved
songs may be played daily for the first week, then every other day
for the next week, then every third day for the third week, etc. An
unlimited number of scheduling variants are possible. The
embodiment described here additionally mixes songs from the user's
favorite satellite channels with stored songs; this is one way the
user hears new songs that he can decide to Save or not.
[0309] Also, since the device described in this embodiment has
iPod-like functionality, the Saved song may be found and played
again at the user's will by means of the iPod's standard navigation
features, including being played automatically in the device's
Shuffle mode.
[0310] So the Save button has easily-understood use and value for
the user. However, it also serves the purpose of being an input for
taste profile data. When the user Saves a song, it goes into his
taste profile. Unsaved songs may not go there, although in some
variations of this embodiment, satellite songs that the user has
heard in their entirety (i.e. he didn't turn the device off, select
a song to play from the device's internal library, switch to
another satellite channel, or perform some other action that cuts
the song off), it is stored in the profile with diminished
mathematical weight. And in some variants, songs that were cutoff
are stored as songs that are disliked.
[0311] If the device is permanently one-way, that is, it never has
a direct or indirect (through a PC) ability to send data onto the
Internet or another network, the taste profile built by the Save
button (and/or other techniques) is never made available to other
users. However, for the local user's benefit, it enables him to
discern which candidate neighbors are received from the satellite
are nearest neighbors, and the device can therefore generate
recommendations in the usual way.
[0312] As the taste profile for the user of the portable device
grows because of the use of the Save button, the recommendations
that can be generated in the usual manner become more and more
accurate.
[0313] The embodiment currently being discussed involves a unique
identifier for each song, which is an md5 hash of a concatenation
of the song artist, name, album defined by the makeSongHash
function in the accompanying code. (Other variants use other
techniques such as fingerprints of the audio data, an md5 hash of a
text representation of the audio data, etc.) This identifier is
contained in the taste profiles for the user and is used as the
representation of the song in that data (or as one such
representation).
[0314] When a song is recommended, it goes into a list in the
device's storage, and is checked against a broadcast schedule
transmitted periodically by the satellites and received by the
device. The device then knows to record certain songs sent on
certain channels in the future, and does so when the time comes,
saving the song data into persistent storage and adding the song to
the device's music library. In this way the device builds a library
of music that the user is likely to enjoy. This music is added to
the user's virtual channel, and also available to play at his will
through the device's song navigation mechanisms.
[0315] With regard to the virtual channel, the result is as if
there were a radio channel dedicated exclusively to that individual
user's tastes, which gets more and more finely tuned over time.
[0316] When the portable device is connected to a personal
computer, for instance via FireWire, USB, BlueTooth, Ethernet or
wi-fi, songs downloaded from the satellite may be transferred to
the computer, either for long-term storage in that computer or
played using the computer's hardware using data only persistently
stored on the device.
[0317] The embodiment currently under discussion can be used in two
modes: subscription and purchase modes.
[0318] In subscription mode, the user pays a set fee per month, and
can store as much music downloaded from the satellites (and/or from
other sources) as well fit in the device's storage, and they may be
played as frequently as the user desires. (In some variants there
is a tiered subscription service, where for a particular monthly
fee, a particular number of songs or artists' music may be stored
persistently or a particular amount of storage may be allocated; or
songs from the satellites may be played only a particular number of
times.)
[0319] In purchase mode, Saving a song causes the song to be
purchased. (In some variants the Save button is labelled
"Purchase".) When the device is eventually connected to a two-way
network or to a wireless-enabled financial "smart card" with debit
capabilities, or to analogous financial technologies, the cost is
deducted.
[0320] Further variants of the embodiment described above:
[0321] Rather than receiving a schedule from the satellites, the
schedule may be received over the Internet or other network for
devices that sometimes have connections to such networks. In
further embodiments, no schedule is available and instead, a
directory is provided of channels together with taste-descriptive
data such as a list of genres that each channel focuses on or a
list of representative artists, which is used to determine which
channels are likely to contain songs the user will want to hear
and/or are or will be recommended.
[0322] In typical embodiments, taste profile data contains genre
info for songs in the taste profile, or the service provides a
look-up table mapping song identifiers to genres. When the channels
have associated genre information, they can use that information
for recommended songs to choose likely channels to listen to to
receive the songs. When information such as representative artists
is used to describe channels, the artists that most frequently
appear in taste profiles having the recommended song can be matched
against the lists of artists describing different channels, and the
channels that best match the currently
recommended-but-not-yet-downloaded songs are the ones that the
device focuses on in waiting for the song to arrive.
[0323] Some embodiments use sonic descriptors of each channel to
describe it. For instance, the companies Savage Beast and
SoundFlavor describe each song by a set of attributes including
such factors as tempo, instrumentation, sex of the singer, and
hundreds of others. Some them are human-generated, and some are
software-generated (the software examines the audio data) or
generated with the aid of software. It is obvious that with such a
collection of attributes, average values or other kinds of
summarizations may be generated for each channel that tends to
describe the music played on that channel. And a vector or other
structure may be provided that enables the attributes associated
with recommended songs to be determined.
[0324] Such structures may be downloaded via the Internet or from
the satellites. On a special channel or interspersed with other
data, the satellites can send the attributes associated with each
song, either at the same time as a song's audio data is
transmitted, or separately; this occurs in preferred
embodiments.
[0325] In some embodiments the attributes associated with songs the
user likes, for instance as signified by pressing the Saved button,
are summarized by software within the handheld device. For example
average values of the attributes can be calculated using arithmetic
or geometric averaging, or only the attributes most frequently
associated with liked songs may be counted, or other summarization
techniques may be used; these comprise a taste profile of the user
instead of, or in addition to, the taste profile built from
identifiers of liked songs (where "liked" songs may also be
signified by being already-owned by the user). In some embodiments
there is an additional input device such as a button that signifies
that the user does not like a song; then the averages and/or
presence/absence counts used to generate the taste profile may be
adjusted negatively by that control in association with a
particular song.
[0326] In some embodiments, each user is associated with a song
attribute, and the value of the attribute depends on whether the
associated user has the song or not, and/or on how often the user
plays the song. So each song has an associated list of attributes
corresponding to users, either instead of or in addition to other
attributes such as ones derived from the sonic content.
[0327] In embodiments where there are too many song attributes to
be downloaded without using too much bandwidth, and where the
attributes are statistically correlated, factor analysis may be
used to reduce the number of attributes into principle components.
Based on a calculations generated on a server or using distributed
systems, the local device can use these calculations to generate
the principal components from locally produced data (such as the
identifiers of the other users who have each song, as determined by
their incoming profiles); these can be summarized to produce a
taste profile for the user. Thus it is possible to arrive at a
manageable number of attributes for individual songs and local
taste profiles. Those of ordinary skill in the art of statistical
factor analysis will see how to do this.
[0328] In many embodiments having attributes associated with each
song (comprising a song taste profile), which correspond well
enough to the attributes of a summarized taste profile for the
local user that similarity can be measured between the two types of
taste profiles, recommendations are generated by using the songs
whose taste profiles most closely match the local user's taste
profile. Thus instead of the process of finding nearest neighbor
users and deriving recommendations from their likes and interests,
the nearest neighbors are themselves recommendable items and the
nearest ones are therefore recommended.
[0329] Some embodiments need no two-way nodes. The portable devices
calculate which incoming songs are nearest-neighbors without any
data from other user nodes. Note that while human input may be used
to decide on the appropriate attribute values for each song, this
input need not be done on "user nodes" as we use the term elsewhere
in this specification. Rather that data may be input through
software specially designed for the manual entry of such data by a
someone whose job it is to do that analysis work.
[0330] To envision a more concrete example of the invention
described in the previous paragraph satellites broadcast taste
profile information for each song. These may be broadcast at the
same time as the songs by interleaving the music data with the song
data or by using another channel, or they may be broadcast at other
times. In a system where a broadcast schedule is broadcast in
advance of broadcasting the songs, it is preferable that the song
taste profiles are broadcast a substantial amount of time before
the songs themselves so that software may automatically schedule
the future recording of very similar songs. As described earlier
the user's local taste profile is refined over time due to input
from a Save button or other passive or active indications of taste,
and the portable device may never have any two-way connectivity. So
using the portable device's CPU to find nearest neighbor songs
based on user taste profiles built up on the local machine and
compatible taste profile broadcast from the satellites produces a
situation where analysis of each song, using human and/or software
input, empowers portable devices to adaptively provide ever more
appropriate listening material for users.
[0331] It should be noted that the above example is for example
only and must not be construed to limit the scope of the invention.
The role of "portable device" in the example may be played by any
CPU-enabled device, including a desktop PC, or a unit built into an
automobile or airplane.
[0332] When the term "satellite" or "satellites" is used in this
specification, it should be noted that whether there is one or more
than one satellite makes no difference from the standpoint of this
invention, although of course a collection of satellites will
provide a broader range of coverage than a single satellite. One
advantage of the techniques described here is that, especially in
embodiments where the song taste profiles are transmitted in close
temporal proximity to the song data, a portable device is enabled
to acquire a library of satellite-downloaded music that the user
may continue to enjoy even if the device goes out of range of the
satellite(s) for some time.
[0333] In another set of variations, no taste profiles are sent
from the satellites. Instead, a software analysis of each song is
done in the portable device itself, determining values for
attributes such as tempo. Software to do this sort of thing exists
today in, for example, the Polyphonic HMI's Hit Song Science
technology. Any engineer of ordinary skill and access to such
software will see how to use integrate it into the present
invention. Thus song taste profiles generated by such software play
the same role as downloaded song taste profiles do in other
embodiments described above. However, there is a substantial
advantage to downloading the song taste profiles: present software
does not have the ability to examine song data for such attributes
as sense of humor in the lyric. There are many such qualities that
pertain to recorded music that software is not currently capable of
analyzing. So embodiments based wholly on software analysis of the
music can be expected not to produce as much user benefit as
embodiments involving at least some human analysis of the songs.
For spoken-word content, speech-to-text software can determine many
of the words spoken, and those can be mapped to content vectors as
is often done for document analysis; that can comprise the item
taste profiles.
[0334] While the above specification focuses on songs for reasons
of example, the same approach will also accrue to spoken-word
recordings, "podcasts" involving music played at intervals with
spoken-word in between, and video and even purely visual
content.
[0335] For example, one set of embodiments is based upon an LCD,
plasma, or nanotube display hanging on a wall. It displays
different images, which may be moving or still, which it receives
from a satellite. Taste profiles are downloaded which contain
attributes pertaining to each visual item; in some embodiments the
taste profiles contain identifiers of other users who like the
visual item supplied by users with two-way network connections; in
other cases or in combination with such human identifiers, taste
profiles containing attributes such as indications of the presence
of various colors, hard or soft edges, and whether the image is
realistic or abstract, landscape or portrait, etc.; in some
embodiments software analysis within the local device produces a
taste profile; for instance such information as color is simple to
extract from digital image data, there is existing software, used
to block pornographic sites, which can discern such characteristics
as the presence of bare human skin.
[0336] There is a Save button that protrudes slightly from a frame
that surrounds the "picture". When the user sees an image he
particularly likes, he presses Save, and then that image is stored
into persistent storage by a CPU which is embedded into the device
and displayed later. The CPU also makes use of that information to
improve a local taste profile representing the user's tastes. This
enables the device to acquire more visual items that the user will
enjoy, as described above for music.
[0337] Another set of variations of the invention here as it
relates to one-way devices but also as it relates to purely two-way
node embodiments described elsewhere in this specification is
similarity matching by means of pattern-matching technologies. For
example, a song would be represented, instead of (or in addition
to) a taste profile containing a list of attributes, by a
pattern-matching software. For instance, it could be represented by
a neural net, with the number of layers and nodes and the numerical
values that are intrinsic to the net being defined in a way that
takes a local user's taste profile information and outputs a high
value if the song is likely to match the user's tastes and a low
value if it is not. As one way of finding the necessary values, the
neural net can be trained using taste profile data of users who had
two-way connections enabling the profiles to be communicated to a
central server. The neural net is trained so that it takes the
taste profiles as input, and outputs high or low values depending
on whether the user that is currently trained on liked the song or
not (for instance based on whether he pressed a Save button or did
nothing or "fast forwarded" past a song and never listened to it in
its entirety; in such a case the net would preferably be trained to
output a numerical value with a high value in the first case, a
middle value in the second, and a low value in the third). In order
that there are input values to train the neural nets, taste
profiles based on song attributes are also provided, and a user
taste profile to be input into the artificial intelligence unit is
generated based on the ones associated with the songs the user
likes (and/or does not like).
[0338] In some embodiments content items such as songs are
accompanied by lists of identifiers of other songs that are
considered to be likely to be enjoyed by the same people as the
current song, as determined, for example, either by having similar
sonic and/or lyrical attributes, or tending to be liked (or
purchased by), the same people. These identifiers may be used as
attributes for nearest-neighbor matching, but they may also be used
as simple indicators that the listed song identifiers may be used
to schedule the acquision of those other songs if the user likes
the current one (as indicated, for example, by pressing a Save
button while listening to it).
[0339] In some embodiments incorporating the virtual channel
concept described above, when the user first starts the player, and
selects a virtual channel, if there aren't many songs (or are no
songs) stored in the device yet, it may start playing the
currently-being-broadcast song from one of the user's favorite
channels, and follow that up with a song from storage if one is
available, or play a song from one of the user's favorite
channels.
[0340] When playing songs from the user's favorite channels, it may
receive broadcasts from more than one channel at a time, and play
one song while simultaneously caching another song from another
channel into RAM of persistent storage; after the first song is
complete it may play a song from another channel.
APPENDIX 6
[0341] This Appendix describes a class of embodiments wherein there
is two way communication between nodes, but it is limited to a
particular geographical area, being enabled by such wireless
technologies as Wi-Fi or Bluetooth or the like.
[0342] When two wireless-enabled portable devices are in close
enough proximity that communications may be automatically
established, a link is set up between the two devices.
(Communications may occur between more than two devices
simultaneously, but for simplicity of example we are focusing the
interactions between one pair at a time.) For instance, a link may
be established between two devices in different automobiles or
between two handheld devices such as cellular phones.
[0343] All or a substantial portion of the music library
identifiers in each device comprises the taste profile of that
devices. It is communicated to the other device by wireless means.
The similarity of the other user to the local user is calculated by
means of the taste profiles (by local user we mean the person whose
information is in one of the two devices). If the other user's
taste profile makes it one of the N most similar ones seen by the
local user's device, where N is a predetermined number, the taste
profile is stored and used for recommendation purposes as described
elsewhere in this specification.
[0344] Note that very similar, but older, taste profiles may be
deleted, and thus there may be more N chosen for storage over the
course of time.
[0345] In preferred embodiments, for a subscription fee, the
devices are allowed to copy music from one device to another. If a
track residing on a device to which the local user is currently
connected has a highly recommended song on it, it is transferred to
the local device either automatically or after suggesting the
transfer and waiting for the user to OK it (for instance, by
pressing a button on the device in response to an onscreen
notification). In other embodiments, the device keeps track of how
many times the user has played a song, and to play it more than
(for instance) three times, the user must buy the track. This
transaction occurs at the time it is connected to the wired
Internet (either through a wireless base station, a direct Ethernet
connection, or a connection via USB, FireWire, or the like to a
desktop PC which is connected to the Internet).
[0346] In further preferred embodiments, the data corresponding to
each song may contain an indicator (such as a bit or particular
byte value) indicating that certain songs are free--in other words
they can be legally transferred between devices without legal or
copyright hindrance. In that case transfers occur as described
above but, in the absence of a paid subscription, only the free
songs may be transferred.
[0347] Practicioners of the art of creating wireless networking
hardware and software, such as Bluetooth and Wi-Fi, will readily
see how to handle the connectivity aspects described in this
Appendix.
INDUSTRIAL APPLICABILITY
[0348] The present invention is desirably implemented at least in
part via a public network or internet, although some embodiments
make use of satellite transmissions and/or wireless transmissions
directly from device to device. It may, for example, be coupled to
a private network or intranet through a firewall server or router.
As used herein, the term "internet" generally refers to any
collection of distinct networks working together to appear as a
single network to a user. The term "Internet", on the other hand,
refers to a specific implementation of internet, the so-called
world wide "network of networks" that are connected to each other
using the Internet protocol (IP) and other similar protocols. The
Internet provides file transfer, remote log in, electronic mail,
news and other services. The system and techniques described herein
can be used on any internet including the so-called Internet.
[0349] One of the unique aspects of the Internet system is that
messages and data are transmitted through the use of data packets
referred to as "datagrams." In a datagram-based network, messages
are sent from a source to a destination in a manner similar to a
government mail system. For example, a source computer may send a
datagram packet to a destination computer regardless of whether or
not the destination computer is currently powered on and coupled to
the network. The Internet protocol (IP) is completely sessionless,
such that IP data gram packets are not associated with one
another.
[0350] The firewall server or router is a computer or item of
equipment which couples the computers of a private network to the
Internet. It may thus act as a gatekeeper for messages and
datagrams going to and from the Internet 1.
[0351] An Internet service provider (ISP) is also coupled to the
Internet. A service provider is an entity that provides connections
to a part of the Internet, for a plurality of users. Also coupled
to the Internet are a plurality of web sites or nodes. When a user
wishes to conduct a transaction at one of the nodes, the user
accesses the node through the Internet.
[0352] For Internet-enabled embodiments, each node is configured to
understand which firewall and node to send data packets to given a
destination IP address. This may be implemented by providing the
firewalls and nodes with a map of all valid IP addresses disposed
on its particular private network or another location on the
Internet. The map may be in the form of prefix matches up to and
including the full IP address.
[0353] Also coupled to the Internet is a server, containing an
information database with representations of user profiles and
associated user identifiers 5. The information may be stored, for
example, as a record or as a file. The information associated with
each particular user is stored in a particular data structure in a
database. One exemplary database structure is as follows. The
database may be stored, for example, as an object-oriented database
management system (ODBMS), a relational database management system
(e.g. DB2, SQL, etc.), a hierarchical database, a network database,
a distributed database (i.e. a collection of multiple, logically
interrelated databases distributed over a computer network) or any
other type of database package. Thus, the database and the system
can be implemented using object-oriented technology or via text
files.
[0354] A computer system on which the system of the present
invention may be implemented may be, for example, a personal
computer running Microsoft Windows, Linux, Apple Macintosh or an
equivalent operating system. Such a computer system typically
includes a central processing unit (CPU), e.g., a conventional
microprocessor, a random access memory (RAM) for temporary storage
of information, and a read only memory (ROM) for permanent storage
of information. Each of the aforementioned components is coupled to
a bus. The operating system controls allocation of system resources
and performs tasks such as processing, scheduling, memory
management, networking, and I/O services. Also coupled to the bus
is typically a non-volatile mass storage device which may be
provided as a fixed disk drive which is coupled to the bus by a
disk controller.
[0355] Data and software may be provided to and extracted from
computer system via removable storage media such as hard disk,
diskette, and CD ROM. For example, data values generated using
techniques described herein may be stored on storage media. The
data values may then be retrieved from the media by the CPU and
utilized to recommend one of a plurality of items in response to a
user's query.
[0356] Alternatively, computer software useful for performing
computations related to enabling recommendations and community by
massively-distributed nearest-neighbor searching may be stored on
storage media. Such computer software may be retrieved from the
media for immediate execution by the CPU or by processors included
in one or more peripherals. The CPU may retrieve the computer
software and subsequently store the software in RAM or ROM for
later execution.
[0357] User input to the computer system may be provided by a
number of devices. For example, a keyboard and a mouse are
typically coupled to the bus by a controller. The computer system
typically also includes a communications adapter which allows the
system to be interconnected to a local area network (LAN) or a wide
area network (WAN). Connections may be wireless or wired, Thus,
data and computer program software can be transferred to and from
the computer system via the adapter, bus and network; although it
should be noted that in embodiments without two-way connectivity,
the device manufacture may load the software onto the device.
* * * * *
References