U.S. patent application number 11/855934 was filed with the patent office on 2008-03-27 for topic based recommender system & methods.
Invention is credited to John Nicholas Gross.
Application Number | 20080077574 11/855934 |
Document ID | / |
Family ID | 39226271 |
Filed Date | 2008-03-27 |
United States Patent
Application |
20080077574 |
Kind Code |
A1 |
Gross; John Nicholas |
March 27, 2008 |
Topic Based Recommender System & Methods
Abstract
A recommendation system is used to provide suggestions in
environments such as message boards, RSS aggregators, blogs and the
like by comparing member interests and creating recommendation
items corresponding to categorized topics or other members. In some
instances a natural language can assist in processing content to
sort it into the appropriate topic bin. An advertising module
cooperates with the system to provide content based ads relevant to
the recommended items.
Inventors: |
Gross; John Nicholas; (San
Francisco, CA) |
Correspondence
Address: |
J. NICHOLAS GROSS, ATTORNEY
2030 ADDISON ST., SUITE 610
BERKELEY
CA
94704
US
|
Family ID: |
39226271 |
Appl. No.: |
11/855934 |
Filed: |
September 14, 2007 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
60826677 |
Sep 22, 2006 |
|
|
|
Current U.S.
Class: |
1/1 ; 705/14.69;
707/999.005; 707/E17.059; 707/E17.134 |
Current CPC
Class: |
G06Q 30/0269 20130101;
G06Q 30/0255 20130101; G06F 16/90324 20190101; G06Q 30/0256
20130101; G06Q 30/02 20130101; G06F 16/24578 20190101; G06F 16/335
20190101; G06Q 30/0273 20130101; G06Q 50/01 20130101; G06F 16/9535
20190101 |
Class at
Publication: |
707/5 ; 705/14;
707/E17.134 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06Q 30/00 20060101 G06Q030/00 |
Claims
1. A method of generating automatic recommendations for content to
a first user with a computing system comprising: (a) identifying a
first content reviewed by the first user with the computing system;
(b) identifying a second content reviewed by a plurality of second
users with the computing system; (c) causing a recommender system
to generate a prediction and/or a recommendation for portions of
said second content which are likely to be of interest to the first
user based on an analysis of said first content and said second
content; wherein said first content and second content includes
materials derived and combined with the computing system as
multidimensional data from at least two of the following content
sources accessed by said first user and said plurality of second
users: 1) a message board; 2) a social network site; 3) a blog; 4)
an RSS feed; 5) a content site; wherein the prediction and/or
recommendation is based on multidimensional data.
2. The method of claim 1 wherein said recommender prediction and/or
recommendation is further based on content authored by said first
user and/or said plurality of second users.
3. The method of claim 1 where at least some of said content is
derived from implicit ratings determined from classifying data
reviewed by the first user and said plurality of second users into
one or more topics or concepts.
4. The method of claim 1 wherein said prediction and/or a
recommendation is based on explicit ratings provided by the first
user and said plurality of second users.
5. The method of claim 4 wherein said explicit ratings are given a
weighting in accordance with a time characteristic.
6. The method of claim 5 wherein weighting increases for older
ratings.
7. The method of claim 5 wherein said weighting is also adjusted
based on a frequency of ratings provided for a particular data
item.
8. The method of claim 1 further including a step: presenting an
advertisement along with said recommendation, which advertisement
is based on a content of said recommendation.
9. A method of generating automatic recommendations for content to
a first user with a computing system comprising: (a) processing a
set of first ratings from the first user for a first data source
with the computing system, which first data source includes at
least one of a human author, a social network contact, a message
board, an RSS feed and/or a web log; (b) processing a set of second
ratings from one or more second users for said first data source
and one or more second data sources with the computing system,
which second data sources also include at least one of a human
author, a social network contact, a message board, an RSS feed
and/or a web log; (c) correlating said set of first ratings and
said set of second ratings with the computing system to identify a
selected set of second users that are suitable as predictors for
said first user; (d) recommending one or more of said second data
sources to said first user based on a correlation of said first
user to said selected set of second users done with the computing
system.
10. The method of claim 9 wherein said correlation is determined by
at least one of collaborative filtering and/or corroborative
filtering.
11. The method of claim 9 wherein said first set of ratings and
said second set of ratings includes implicit ratings data which is
determined implicitly from actions taken by said first user and
said set of second users in reviewing content presented
electronically during an Internet session.
12. The method of claim 9 wherein said set of first ratings and
said set of second ratings are recommendations given to authors of
message board posts.
13. The method of claim 9 further including a step: presenting an
advertisement to said first user which contains content predicted
based on said correlation.
14. A method of generating automatic recommendations for content to
a first user with a computing system comprising: (a) providing a
database correlating a plurality of individual data items and
rankings for a first user, wherein at least some of said individual
data items represent human individuals; (b) identifying first
content presented to the first user with the computing system; (c)
identifying a first rating provided by said first user with the
computing system for said first content which is related to least a
first one of said plurality of individual data items; (d)
identifying second content presented to said first user with the
computing system; (e) identifying a second rating provided by said
first user with the computing system for said second content which
is related to least a second one of said plurality of individual
data items; (f) repeating steps (a) through (e) for one or more
second users; (g) comparing ratings provided by said first user and
one or more second users for said plurality of individual data
items to identify correlations between such users and/or items; (h)
generating a prediction and/or a recommendation for the first user
concerning at least a third data item based in part on step
(g).
15. The method of claim 14 wherein said data items are human
perceivable media items.
16. The method of claim 15 wherein said data items are movies.
17. A method of generating automatic recommendations for content to
a first user with a computing system comprising: (a) providing a
first database correlating a plurality of individual data items and
rankings for a first user, wherein at least some of said individual
data items represent human individuals; (b) providing a second
database correlating a plurality of topics or concepts to one or
more of said plurality of individual data items; (c) identifying
first content presented to the first user with the computing
system; (d) analyzing said first content to identify one or more of
said plurality of topics or concepts and any corresponding
individual data item; (e) identifying a rating provided by said
first user with the computing system for said first content; (f)
generating a ranking for said corresponding individual data item
from said rating for said first content; (g) comparing rankings
provided by said first user and one or more second users for said
plurality of individual data items to identify correlations between
such users and/or items; (h) generating a prediction and/or a
recommendation for the first user concerning a data item based in
part on step (g).
18. The method of claim 17 wherein step (d) is performed by a
natural language engine classifier.
19. The method of claim 18 further including a step: training said
natural language engine with a training corpus.
20. The method of claim 17 where said first content includes at
least one of: a) an advertisement presented to the first user; b) a
search result list; c) human readable content reviewed on the
Internet.
21. The method of claim 17 further including a step: customizing a
search engine result by the first user concerning one of said
topics or concepts based on said prediction and/or
recommendation.
22. The method of claim 1 further including a step: presenting an
advertisement along with said recommendation, which advertisement
is based on a content of said recommendation.
23. A method of generating automatic recommendations for content to
a first user with a computing system comprising: (a) processing a
set of first ratings from the first user for a first data source
with the computing system, which first data source includes at
least one of a human author, a social network contact, a message
board, an RSS feed and/or a web log; wherein said set of first
ratings are weighted by at least one of the following factors: 1)
time; and/or 2) frequency; (b) processing a set of second ratings
from one or more second users for said first data source and one or
more second data sources with the computing system, which second
data sources also include at least one of a human author, a social
network contact, a message board, an RSS feed and/or a web log;
wherein said set of second ratings are also weighted by at least
one of the following factors: 1) time; and/or 2) frequency; (c)
correlating said set of first ratings and said set of second
ratings with the computing system to generate groups of users
and/or groups of data sources suitable for a recommender system;
(d) generating a recommendation with the recommender system to said
first user for one of said second data sources based on step
(c).
24. The method of claim 23 further including a step: customizing a
search engine result by the first user based on said prediction
and/or recommendation.
25. The method of claim 23 further including a step: presenting an
advertisement along with said recommendation, which advertisement
is based on a content of said recommendation.
26. A method of presenting advertising content in connection with
an automatic recommendation to a user comprising: (a) identifying
content presented to a plurality of users; (b) processing said
content with a natural language engine to classify and map such
content to one or more topics; (c) correlating a set of ad items to
said one or more topics; (d) causing a recommender system to
generate a prediction and/or a recommendation for a user, said
recommendation being related to one or more of said topics; (e)
presenting one of said set of ad items to the user as part of said
prediction and/or recommendation.
27. The method of claim 26 further including a step: generating
implicit ratings for said content based on behavior of said
plurality of users.
28. The method of claim 27 further including a step: weighting said
implicit ratings based on a time and/or a frequency of such
ratings.
29. The method of claim 26 wherein said recommendation is related
to a data source, including one of a human author, a social network
contact, a message board, an RSS feed and/or a web log;
30. A method of generating automatic recommendations to a first
user in connection with an online message board with a computing
system comprising: (a) identifying a first set of electronic
messages on the online message board reviewed by the first user
with the computing system; (b) identifying a second set of
electronic messages on the online message board reviewed by a
plurality of second users with the computing system; (c) evaluating
a first set of ratings provided by the first user in connection
with said first set of electronic messages and a second set of
ratings provided by said plurality of second users for said second
set of messages with the computing system; wherein said first set
of ratings and said second set of ratings can be generated by at
least one of an explicit rating and/or an implicit rating, which
implicit rating is derived from online actions taken by said first
user and said plurality of second users; (d) generating a
prediction and/or a recommendation for the first user from said
first set of ratings and said second set of ratings which
identifies at least one of: 1) one or more of said plurality of
second users which are likely to be of interest to the first user;
2) one or more electronic messages which are likely to be of
interest to the first user; 3) one or more electronic message
authors which are likely to be of interest to the first user.
31. The method of claim 30 further including a step: customizing a
search engine result by the first user based on said prediction
and/or recommendation.
32. The method of claim 30 further including a step: presenting an
advertisement along with said recommendation, which advertisement
is based on a content of said recommendation.
33. The method of claim 30 further including a step: presenting an
advertisement to said first user while he/she is reviewing an
electronic message, which advertisement is based both on said first
set of ratings as well as content of said electronic message.
34. The method of claim 30 wherein at least one of said explicit
ratings and/or implicit ratings are given a weighting in accordance
with a time characteristic.
35. The method of claim 34 wherein weighting increases for older
ratings.
36. The method of claim 35 wherein said weighting is also adjusted
based on a frequency of ratings provided for a particular data
item.
37. The method of claim 30 wherein additional content reviewed by
said first user and said plurality of second users at a separate
website from said online message board as well as corresponding
ratings for such content are also evaluated in determining said
recommendation.
38. The method of claim 30 wherein said ratings are associated with
at least one of: a user recommendation for an electronic message; a
user designation of a preferred author for electronic messages; a
user designation of an ignored author for electronic messages; a
user recommendation for a particular topic; a user time spent
reviewing an electronic message; a user search for a set of
electronic messages; an ad selected by a user while reviewing an
electronic message; a number of instances which a user has reviewed
a selected electronic message.
39. The method of claim 30 including a step: identifying and
publishing lists of groups of users with common ratings
behavior.
40. The method of claim 30 including a step: identifying individual
groups of users with common ratings behavior and providing
suggestions to such groups for new memberships.
Description
RELATED APPLICATION DATA
[0001] The present application claims the benefit under 35 U.S.C.
119(e) of the priority date of Provisional Application Ser. No.
60/826,677 filed Sep. 22, 2006 which is hereby incorporated by
reference herein.
FIELD OF THE INVENTION
[0002] The present invention relates to electronic recommendation
systems and other related systems.
BACKGROUND
[0003] Recommender systems are well known in the art. In one
example, such systems can make recommendations for movie titles to
a subscriber. In other instances they can provide suggestions for
book purchases, or even television program viewing. Such algorithms
are commonplace in a number of Internet commerce environments,
including at Amazon, CDNOW, and Netflix to name a few, as well as
programming guide systems such as TiVO.
[0004] Traditionally recommender systems are used in environments
in which a content provider is attempting to provide new and
interesting material to subscribers, in the form of additional
products and services. In some cases (see eg. U.S. Pat. No.
6,493,703 incorporated by reference herein) recommenders have been
employed for the purpose of informing members of an online
community of content and/or preferences of other members.
Nonetheless the use of recommenders has not been extended fully to
such domains and other online areas, including social networks,
which could benefit from such systems. Only recently for example
have recommenders been proposed for generating user to user
recommendations in a music related community. See e.g., US
Publication No. 2007/0203790 to Torrens, incorporated by reference
herein. Similar systems which recommend content/users are described
in U.S. Pat. No. 6,493,703 to Knight et al., also incorporated by
reference herein.
[0005] Multi-dimensional recommenders have also been recently
introduced. For an example of such systems, please see U.S. Patent
Publication No. 2004/0103092 to Tuzhilin et al. and an article
entitled "Incorporating Contextual Information in Recommender
Systems Using a Multidimensional Approach" to Adomavicius et al.,
both of which are hereby incorporated by reference herein. In such
systems, however, the extra dimensionality arises from additional
content related to items which are nonetheless still traditional
commerce items, such as movies.
SUMMARY OF THE INVENTION
[0006] An object of the present invention, therefore, is to reduce
and/or overcome the aforementioned limitations of the prior art. A
recommender system which evaluates multiple data sources is
employed to generate more accurate and relevant predictions
concerning data items and other users within a community.
DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is an illustration of a multi-dimensional recommender
system of the present invention.
DETAILED DESCRIPTION
[0008] FIG. 1 illustrates an example of a preferred embodiment of a
multi-dimensional recommender system 100. A user/item compiler and
database 110 includes a schema in which ratings for individual
items by individual users are identified in a typical matrix
fashion well-known in the art. The primary difference, in this
instance, is that the items are not products/services (i.e., books,
movies, etc.) as in the prior art, but instead represent more
generalized concepts, such as a rating identified by a user for an
author, a social network contact, a particular message board or
post, a particular blog or website, a particular RSS Feed, etc., as
shown by the data received from sources.
Explicit Endorsement Data Sources 120
[0009] As an example of an explicit data source 120, in a typical
message board application such as operated by Yahoo! (under the
moniker Yahoo Message Boards) or the Motley Fool, users are
permitted to designate "favorite" authors, and/or to "recommend"
posts written by particular individuals. In accordance with the
present invention these designations of favorite authors and
recommendations for posts are monitored, tabulated, and then
translated into ratings for such authors/posts and compiled in a
database under control of an item/user compiler module. The ratings
will be a function of the environment in which the information is
collected of course, so that a recommendation by person A for a
post written by person B can be scored as a simple 1 or 0. While
current message board systems presently track these kinds of
endorsements, it will be understood that the invention can be
applied to any aspect of such environments in which subscribers are
allowed to endorse, rate, or declare an interest or preference for
a certain author, post, subject, etc.
[0010] The purpose of using a recommender algorithm (either
collaborative filter or content filter as the case may require)
would be of course to recommend additional authors, topics, or
similar subject matter to members of such message boards based on
their professed interests in other authors and topics. For example
a first individual with favorite authors A, B, C may not realize
that other individuals designating A, B, C as favorite authors also
designate D and E as favorite authors, and this information can be
passed on to such first individual increase the potential enjoyment
of such site.
[0011] Similarly in other environments as data source a user's
designation of favorite web-logs (blogs), favorite RSS feeds, etc.
as evidenced by their inclusion in an RSS aggregator or as
designated favorites within a web browser, or by some other
mechanism could be similarly tabulated to create a user-item matrix
of ratings for such items. This can be used to pass on
recommendations for new blogs, RSS feeds, etc.
[0012] In some applications an e-commerce site includes social
networking features whereby members link to each other explicitly
as part of groups. For example in sites operated by Myspace, or
Netflix, members can designate other members explicitly with the
label friends. As with the other data sources, these user-friend
associations can be tabulated into a form suitable for use by a
recommendation algorithm. Again, while these sites specifically
designate individuals as friends, other sites may allow members to
designate some other favorite item, such as an image, a website, a
video, etc.
[0013] It should be apparent therefore that the item/user compiler
database may in fact be comprised of several different dedicated
files unique to a particular site or domain of users.
Implicit Endorsement Data Sources 125
[0014] In contrast to explicit data sources, the data from implicit
data sources 125 includes materials which typically must undergo
further processing to determine both the item and the associated
rating. That is, in the case of a search result for example, the
item may be one of the pages presented in the search result, or one
or more concepts derived from the content of such page. The rating
may be based on a number of invocations of such page, a length of
time spent at such page, or any other well-known attention metric
used to determine a person's interest in a particular website.
[0015] Other sources of implicit data can include ads selected by
an individual (during an online session or from another electronic
interface which collects and presents ad related data, such as a
Tivo box or the like), audio/video content, posts, blogs, podcasts,
articles, stories and the like which are read and/or authored by
the person. Those skilled in the art will appreciate that such
monitorings could be done in any situation where a person's
selections can be identified.
Natural Language Classifier 130
[0016] Regardless of the source of the implicit data, the invention
uses a natural language classifier/mapper module 130 to translate
the raw data into one or more predefined concepts--representing the
items in this instance--with reference to a topic/concept
classification database 140. For example, a topic/concept may
include such items as personal interests/hobbies, music bands,
company names, stock symbols, brand names, foods, restaurants,
movies, etc., depending on the intended application. These are but
examples of course and it will be understood that such
topics/concepts could include almost anything.
[0017] The items for the recommender database 140 can be mapped
onto the topics/concepts either on a 1:1 basis, a 1:N basis, or an
N:1 basis. In other words, if an item in the recommender database
140 is designated with the label "Sony," there may be an identical
entry in the topic/concept classification with such term. Semantic
equivalents may also be used where appropriate. Similarly a single
item "Sony" may be associated with multiple topics/concepts, such
as a reference to a particular product or service offered by such
company (for example Vaio) a stock symbol for Sony, a reference to
a key employee/officer of Sony, and the like. Conversely some
topics/concepts may also be mapped to multiple items, so that a
reference to Sony Vaio may be linked to such items as Sony and
personal state of the art computers.
[0018] The natural language classifier/mapper 130 is preferably
trained with a training corpus 145 so that it can effectively learn
the correct correlations between data and concepts. After training,
the natural language classifier/mapper 130 can recognize
words/phrases within a search page, ad, post, etc., and correlate
them to one or more topics/concepts. Thus if a document contains
the word Dell, the NL classifier can be taught to recognize such
word as corresponding to such concepts as a particular brand name,
a computer company, and the like.
[0019] The advantage of such approach, of course, is that documents
authored/reviewed by individuals do not have to contain specific or
explicit references to the item in question. Thus the system
understands that an individual reading articles about Porsches,
Ferraris, etc, is probably interested in high end sports cars,
luxury items, etc. While NL classifiers are well-known and have
been used in other contexts such as search engines and related
indices, they do not appear to have been used to date to assist in
the identification and rating of items for a recommender.
Ratings
[0020] As alluded to earlier the ratings in the above types of
applications can be based on any convenient scale depending on the
source of the data and the intended use. Some designations may be
rated or scaled higher than others, depending on their recency,
relative use, etc. The weightings again can be based on system
performance requirements, objectives, and other well-known
parameters. Thus with all other things being equal, older
designations may receive higher scores than more recent
designations, so long as the former are still designated as active
in the user's day to day experience. So for example, after a
predefined period, the first designated favorite author for a
particular individual may receive a boosting to their rating if
such author is still being read by the individual. Similarly,
"stale" endorsements may be reduced over time if they are not
frequently used. The degree of activity may be benchmarked to cause
a desired result (i.e., endorsements receiving no activity within N
days may receive a maximum attenuation factor) monitored to
attenuate the ratings.
[0021] Quantitatively, the ratings therefore can be a simple
mathematical relationship of usage frequency and age of the
endorsement. The ratings may also be affected by the context in
which they are generated, or in which the recommendation is
solicited, as noted in the Tuzhilin materials above. The ratings
can be updated at any regular desired interval of time, such as on
a daily, weekly, or other convenient basis. For example, one
approach may use the product of (frequency of use * age of the
endorsement), with some normalization applied. This will result in
an increase in score for older and more frequently used items.
Other types of algorithms will be apparent to those skilled in the
art. In this respect the invention attempts to mimic the behavior
of a learning network which gives precedence to connections which
are more strongly connected and reinforced regularly.
Recommendation Engine Module 115 Outputs
[0022] A recommendation engine module 115 thus generates outputs in
a conventional fashion using a collaborative filtering algorithm, a
content based filtering algorithm, or some combination therefore
depending on the particular application and the data available in
the item/user database. The outputs can include:
[0023] 1) predictions on how much particular users will like
particular items; for example, in a message board application, an
indication of a rating at output 180 that a particular person would
give to a specific post, specific author, specific topic, etc.;
[0024] 2) recommendation outputs 170 on specific authors, topics,
posts, etc. which a particular person may want to consider for
review in their perusings at such site; this data can be presented
to a user in the form of individual entries, top x lists, etc.
[0025] 3) an output to adjust, adapt or personalize search engine
(not shown) results presented to a user in response to a query on a
specific subject. For example if a user performed a search at a
site relating to video recorders, the result set typically includes
a set of N distinct hits. The information from the recommendation
engine 115 may be used to tailor the results more particularly to
the user.
[0026] In a first instance, the user has a prior profile which can
be determined and exploited from item/user database 110, so that
the search results are modified accordingly. As an example, the
user may have expressed a favorable interest, endorsement or
inclination towards Sony. This data in turn could be used to
optionally modify, bias or alter the N distinct hits to accommodate
the prior experiences.
[0027] In a second instance, even if the user does not have a
profile, the query can be compared against items in the item/user
database to determine favored or highly rated articles. Thus, in
the above example, any ratings for Sony, or other video recorder
suppliers, could be evaluated to identify additional modifications
to the search engine results. In this manner a recommender can
supplement the performance of a search engine based on real world
experiences and thus increase the chances of successful experiences
by searchers.
[0028] To map search queries to items for the above enhancements,
the topic/concept classification database 140 can be consulted as
needed. Again this may result in a number of item related entries
being used to modify the search results.
[0029] It should be apparent that the output could be used by a
separate recommender system, as well, to supplement an existing
data set.
Advertising Module 150
[0030] An advertising module 150 can be used to provide relevant
advertising material based on the content of predictions,
recommendations and other outputs of the recommendation engine. As
seen in FIG. 1, an interface routine 153 permits third parties and
site operators to enter well-known advertising campaign
information, such as advertising copy/content, desired keywords,
and other information well-known in the art. The ads can take any
form suitable for presentation within an electronic interface, and
may include text and multi-media information (audio, video,
graphics, etc.)
[0031] In prior art systems ads are correlated to search engine
results, such as in a system known as "Adwords" offered by Google.
In such applications ads are presented to searchers based on one or
more topics identified in a search query.
[0032] The present invention extends this concept to recommenders,
so that ads are served in accordance with a topic determined from a
recommendation. For example, on a message board application, if the
system were to determine that (based on prior ratings for certain
topics) the user should also be recommended to review content on a
board devoted to vintage cars, the ads presented with such
recommendation could be tailored to content of such vintage car
board, and/or to the specific content of the recommendation
itself.
[0033] As seen in FIG. 1, the advertising stock 152 offered by
third parties is matched against one or more topics/concepts in the
topic/concept classification database 140. The mapping of the
advertising stock to such topics can again be done automatically by
natural language classifier/mapper 130, or alternatively selected
independently by the third party/system site operator. In the
latter case some oversight may be necessary to prevent third
parties from intentionally polluting the relevancy of ads by
presenting them in inappropriate contexts.
[0034] An advertising engine 151 is invoked and cooperates with a
recommendation engine 115 so that relevant ads are presented with
an output of the latter. As noted above such ads may also be
presented as suitable for inclusion with a modified set of search
results for a search engine. In this fashion an advertising system
can be superimposed over the recommender system, so that relevant
ads are presented at 160 in response to, and in conjunction with, a
recommendation, prediction, etc., either at the same time, or at a
later time in the form of emails, alerts, printed copy or other
suitable materials for consumer consumption.
Applications
[0035] As alluded to earlier, the present invention can be used
advantageously in a number of e-commerce applications, including:
[0036] Message boards: the invention can be employed to
predict/recommend other authors, posters, topics, etc., which would
be of interest to members; [0037] Social networking: the invention
can be employed to predict/recommend other contacts, "friends,"
topics, etc. which a member of an online community may enjoy based
on such member's other friends, topics reviewed, etc. By measuring
an adoption rate between members for particular friends, or
determining which friends' interests are most often copied, the
system can even provide suggestions to specific members so that
they send invitations to other members predicted to be good
candidates for friends within the community. [0038] RSS, Blogs,
Podcasts, Ads: the invention can be employed to predict/recommend
other Ads, RSS feeds, Blogs and Podcasts to individuals, based on
adoptions/endorsements made by other online users.
[0039] Furthermore other options include monitoring group behavior
and treating any such collection of individuals as a single entity
for item/rating purposes. This aggregation can be used to recommend
higher order logical groupings of individuals, particularly in
social networking applications, to enhance the user experience.
[0040] That is, in conventional CF systems, individuals are
automatically assigned to specific clusters based on a
determination of a significant number of common interests/tastes.
In the present invention the individual self-selected groupings
within social networks can be broken down and treated as clusters
so that comparisons can be made against particular user's
interests, predilections, etc. Based on such comparisons groups can
opt to extend invitations to new members which they would otherwise
not notice or come into contact with. Conversely new members can be
given some immediate insight into potentially fruitful social
groups.
[0041] It will be understood by those skilled in the art that the
above is merely an example and that countless variations on the
above can be implemented in accordance with the present teachings.
A number of other conventional steps that would be included in a
commercial application have been omitted, as well, to better
emphasize the present teachings.
[0042] It will be apparent to those skilled in the art that the
modules of the present invention, including those illustrated in
FIG. 1 can be implemented using any one of many known programming
languages suitable for creating applications that can run on large
scale computing systems, including servers connected to a network
(such as the Internet). The details of the specific implementation
of the present invention will vary depending on the programming
language(s) used to embody the above principles, and are not
material to an understanding of the present invention. Furthermore,
in some instances, a portion of the hardware and software of FIG. 1
will be contained locally to a member's computing system, which can
include a portable machine or a computing machine at the users
premises, such as a personal computer, a PDA, digital video
recorder, receiver, etc.
[0043] Furthermore it will be apparent to those skilled in the art
that this is not the entire set of software modules that can be
used, or an exhaustive list of all operations executed by such
modules. It is expected, in fact, that other features will be added
by system operators in accordance with customer preferences and/or
system performance requirements. Furthermore, while not explicitly
shown or described herein, the details of the various software
routines, executable code, etc., required to effectuate the
functionality discussed above in such modules are not material to
the present invention, and may be implemented in any number of ways
known to those skilled in the art.
[0044] The above descriptions are intended as merely illustrative
embodiments of the proposed inventions. It is understood that the
protection afforded the present invention also comprehends and
extends to embodiments different from those above, but which fall
within the scope of the present claims.
* * * * *