U.S. patent application number 14/209062 was filed with the patent office on 2015-09-17 for content preview generation using social network analysis.
This patent application is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Lorraine M. HERGER, Neal M. KELLER, James R. KOZLOSKI, Matthew A. McCARTHY, Clifford A. PICKOVER.
Application Number | 20150264092 14/209062 |
Document ID | / |
Family ID | 54070289 |
Filed Date | 2015-09-17 |
United States Patent
Application |
20150264092 |
Kind Code |
A1 |
HERGER; Lorraine M. ; et
al. |
September 17, 2015 |
CONTENT PREVIEW GENERATION USING SOCIAL NETWORK ANALYSIS
Abstract
Disclosed is a system and method for generating a preview of a
digital content item using social network analysis. Members of a
social network who acquire the digital content item may identify
interesting portions of the digital content. When a member of the
social network requests a preview of the digital content item,
typically in anticipation of an acquisition of the digital content
item, the interesting portions of the digital content item
identified by fellow social network members are considered in the
generation of the preview. Selection of the interesting content for
preview may include more identified content, as well as social
network relationship and role magnitudes. The digital content item
may include: text, such as books or articles; multimedia such as
audio/video; and interactive, such as games or virtual worlds.
Inventors: |
HERGER; Lorraine M.; (Port
Chester, NY) ; KELLER; Neal M.; (Pleasantville,
NY) ; KOZLOSKI; James R.; (New Fairfield, CT)
; McCARTHY; Matthew A.; (Holly Springs, NC) ;
PICKOVER; Clifford A.; (Yorktown Heights, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION
ARMONK
NY
|
Family ID: |
54070289 |
Appl. No.: |
14/209062 |
Filed: |
March 13, 2014 |
Current U.S.
Class: |
709/204 |
Current CPC
Class: |
H04L 65/403 20130101;
H04L 67/22 20130101; H04L 51/32 20130101; G06F 40/169 20200101;
G06Q 50/01 20130101; G06Q 30/0631 20130101; G06F 17/3089 20130101;
G06F 16/9535 20190101; H04L 67/306 20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06; G06F 17/24 20060101 G06F017/24; G06F 17/30 20060101
G06F017/30 |
Claims
1. A method operating within a content distribution server
comprising: identifying a first interesting portion of a digital
content item having a plurality of portions, the first interesting
portion identified based upon a first input signal received at a
first content presentation device, the first content presentation
device operated by a first person associated with a first social
network; identifying a second interesting portion of the digital
content item, the second interesting portion identified based upon
a second input signal received at a second content presentation
device, the second content presentation device operated by a second
person associated with a second social network; determining that a
third person operating a third content presentation device is
associated with the first social network; determining that a fourth
person operating a fourth content presentation device is associated
with the second social network; generating a first digital content
preview of the digital content item for presentation on the third
content presentation device, the first digital content preview
including the first interesting portion based upon the third person
being associated with the first social network; and generating a
second digital content preview of the digital content item for
presentation on the fourth content presentation device, the second
digital content preview including the second interesting portion
based upon the fourth person being associated with the second
social network.
2. The method according to claim 1 further comprising: enabling
presentation of the first digital content preview by the third
content presentation device; receiving an acquisition signal
associated with the presentation of the first digital content
preview; and enabling presentation of the plurality of portions of
the digital content item by a content presentation device operated
by the third person based upon the acquisition signal.
3. The method according to claim 2 further comprising communicating
information related to a second digital content item to the first
person based upon the acquisition signal.
4. The method according to claim 1 wherein the digital content item
includes text information having a plurality of passages
corresponding to the plurality of portions and the first
interesting portion corresponds to at least a first of the
plurality of passages and the first input signal is based upon a
first annotation to the text information received from the first
person at the first content presentation device.
5. The method according to claim 4 wherein the first annotation
includes at least one of text highlights, interlineations and
bookmarks.
6. The method according to claim 1 wherein the digital content item
includes text information having a plurality of passages
corresponding to the plurality of portions and the first
interesting portion corresponds to at least a first of the
plurality of passages and the first input signal is based upon an
amount of time spent reading the first of the plurality of passages
by the first person at the first presentation device.
7. The method according to claim 1 wherein the digital content item
includes multimedia information having a plurality of multimedia
portions corresponding to the plurality of portions and the first
interesting portion corresponds to at least a first of the
plurality of multimedia portions and the first input signal is
based upon at least one of replaying, voting and highlight made on
the first of the plurality of multimedia portions by the first
person at the first presentation device.
8. The method according to claim 1 wherein the digital content item
includes a plurality of interactive portions corresponding to the
plurality of portions, wherein presentation of a subsequent
interactive portion is based upon inputs received during
presentation of a prior interactive portion and the first
interesting portion corresponds to at least a first of the
plurality of interactive portions and the first input signal is
based upon at least one of replaying, voting and highlight made on
the first of the plurality of interactive portions by the first
person at the first presentation device.
9. The method according to claim 1 wherein the identifying the
first interesting portion identifies the first interesting portion
based upon a first plurality of input signals, the first plurality
of input signals including the first input signal, identifying the
first interesting portion, and received at a first plurality of
content presentation devices including the first content
presentation device, the first plurality of content presentation
devices operated by a first plurality of persons including the
first person, the first plurality of persons associated with the
first social network.
10. The method according to claim 9 wherein the first plurality of
input signals are included within a first multiplicity of input
signals identifying a first multiplicity of interesting portions
including the first interesting portion, the first plurality of
content presentation devices are included with a first multiplicity
of content presentation devices, the first plurality of persons are
included with a first multiplicity of persons operating the first
multiplicity of content presentation devices, the first
multiplicity of persons being associated with the first social
network, and the identifying the first interesting portion further
includes determining the first plurality of input signals to
indicate that the first interesting portion is a more identified
portion of the first multiplicity of interesting portions
identified by the first multiplicity of input signals.
11. The method according to claim 10 wherein the identifying does
not identify the first interesting portion if the first plurality
of input signals identifying the interesting portion is not equal
to or greater than two.
12. The method according to claim 1 further comprising: identifying
a third interesting portion of the digital content item, the third
interesting portion identified based upon a fifth input signal
received at a fifth content presentation device, the fifth content
presentation device operated by a fifth person associated with the
first social network, wherein, the determining further determines a
first relationship magnitude between the third person and the first
person and a second relationship magnitude between the third person
and the fifth person, and the generating generates the digital
content preview to include the first interesting portion based upon
the third person being associated with the first social network and
the first relationship magnitude being greater than the second
relationship magnitude, the second interesting portion based upon
the third person being associated with the second social network,
or the fourth interesting portion based upon the third person being
associated with the first social network and the second
relationship magnitude being greater than the first relationship
magnitude.
13. The method according to claim 12 wherein the determining the
first and the second the relationship magnitude includes evaluating
at least one of a degree centrality, betweenness centrality,
closeness, Eigenvalue hub and authority.
14. The method according to claim 12 further wherein the
relationship magnitude includes at least one of a family
relationship, an employment relationship, a social media
relationship status, and an interest in a topic related to the
digital content item.
15. The method according to claim 1 further comprising: identifying
a third interesting portion of the digital content item, the third
interesting portion identified based upon a fifth input received at
a fifth content presentation device, the fifth content presentation
device operated by a fifth person associated with the first social
network, wherein, the determining further determines a first role
magnitude of the first person within the first social network and a
second role magnitude of the fifth person within the first social
network, and the generating generates the digital content preview
to include the first interesting portion based upon the third
person being associated with the first social network and the first
role magnitude, the second interesting portion based upon the third
person being associated with the second social network, or the
fourth interesting portion based upon the third person being
associated with the first social network and the second role
magnitude.
16. The method according to claim 15 wherein the digital content
item is included within a multiplicity of digital content items
having a plurality of subject classifications and the first role
magnitude is based upon a first analysis of the multiplicity of
digital content items having interesting portions identified by the
first person and included with a subject classification associated
with the digital content item, and the second role magnitude is
based upon a second analysis of the multiplicity of digital content
items having interesting portions identified by the fifth person
and included with the subject classification associated with the
digital content item.
17. A content distribution server comprising: a content distributor
having a digital memory for storing a digital content item having a
plurality of portions, the content distributor for distributing the
digital content item to a first content presentation device and
second content presentation device; an interesting portion analyzer
coupled to the first and second content presentation devices for
receiving a first input signal from the first content presentation
device indicative of a first interesting portion of the plurality
of portions of the digital content identified by a first person
included within a first social network; receiving a second input
signal from the second content presentation device indicative of a
second interesting portion of the plurality of portions of the
digital content identified by a second person included within a
second social network; a social network analyzer coupled to the
first and second content presentation devices and a third content
presentation device for determining if a third person operating the
third content presentation device is included in either the first
social network or the second social network; and a digital content
preview generator coupled to the third content presentation device
for generating a first digital content preview of the digital
content item including the first interesting portion and not the
second interesting portion based upon the third person being a
member of the first social network, and for generating a second
digital content preview of the digital content item including the
second interesting portion and not the first interesting portion
based upon the third person being a member of the second social
network.
18. The content distribution server according to claim 17 wherein
the digital content preview generator is further coupled to a
fourth content presentation device for selecting a preview of the
digital content item excluding the first interesting portion and
the second interesting portion if a fourth person operating the
fourth content presentation device is not associated with either
the first social network or the second social network.
19. The content distribution server according to claim 18 wherein
the interesting portion analyzer is further coupled to a fifth
content presentation device for receiving a third input signal from
the fifth content presentation device indicative of a third
interesting portion of the plurality of portions of the digital
content item identified by a fifth person included within the first
social network, the social network analyzer is further coupled to
the fifth content presentation device for determining a first
relationship magnitude between the third person and the first
person and a second relationship magnitude between the third person
and the fifth person, and the digital content preview generator
generates the digital content preview to include the first
interesting portion based upon the third person being associated
with the first social network and the first relationship magnitude
being greater than the second relationship magnitude, the second
interesting portion based upon the third person being associated
with the second social network, or the third interesting portion
based upon the third person being associated with the first social
network and the second relationship magnitude being greater than
the first relationship magnitude.
20. The content distribution server according to claim 18 wherein
the interesting portion analyzer is further coupled to a fifth
content presentation device for receiving a third input signal from
the fifth content presentation device indicative of a third
interesting portion of the plurality of portions of the digital
content item identified by a fifth person included within the first
social network, the social network analyzer is further coupled to
the fifth content presentation device for determining a first role
magnitude of the first person within the first social network and a
second role magnitude of the fifth person within the first social
network, and the digital content preview generator generates the
digital content preview to include the first interesting portion
based upon the third person being associated with the first social
network and the first role magnitude being greater than the second
role magnitude, the second interesting portion based upon the third
person being associated with the second social network, or the
third interesting portion based upon the third person being
associated with the first social network and the second role
magnitude being greater than the first role magnitude.
Description
BACKGROUND
[0001] The present description generally relates to generating
previews of content based upon the social network analysis.
[0002] The internet and other computer based networks have become
an effective media for the distribution of digital content items
such as books, movies and interactive games. As part of the
distribution of such content, previews are provided to potential
customers browsing the content. Previews are abbreviated portions
of the content that attempt to highlight most interesting portions
of the content and are typically generated for a large number of
potential customers. The highlighted content may not actually be
the most interesting portion of the content to any particular
potential customer.
[0003] Once delivered, the interesting portions of the content are
identified by those consuming the content. Interesting passages in
a book are highlighted, favorite movie scenes are flagged or
favorite locations in a virtual world or game are identified. This
identification can be done on content presentation devices such as
e-readers, personal computers, and video game consoles. However, an
interesting portion of content identified by one person may not be
a very interesting portion of the content to another person.
[0004] Content consumers may be members of any of a multiplicity of
social networks that share a common interest. Generating content
previews that highlight interesting portions of the content for a
particular individual previewing the content may help the
individual make a more informed decision before acquiring and
investing time in consuming the content.
SUMMARY
[0005] Disclosed is a novel system and method generating a preview
of a digital content item based upon social network relationships
and interesting portions of the digital content item identified by
social network members.
[0006] In one example, a method operating within a content
distribution server comprises identifying a first interesting
portion of a digital content item having a plurality of portions,
the first interesting portion identified based upon a first input
signal received at a first content presentation device, the first
content presentation device operated by a first person associated
with a first social network; identifying a second interesting
portion of the digital content item, the second interesting portion
identified based upon a second input signal received at a second
content presentation device, the second content presentation device
operated by a second person associated with a second social
network; determining that a third person operating a third content
presentation device is associated with the first social network;
determining that a fourth person operating a fourth content
presentation device is associated with the second social network;
generating a first digital content preview of the digital content
item for presentation on the third content presentation device, the
first digital content preview including the first interesting
portion based upon the third person being associated with the first
social network; and generating a second digital content preview of
the digital content item for presentation on the fourth content
presentation device, the second digital content preview including
the second interesting portion based upon the fourth person being
associated with the second social network.
[0007] In another example, a content distribution server comprises:
a content distributor having a digital memory for storing a digital
content item having a plurality of portions, the content
distributor for distributing the digital content item to a first
content presentation device and second content presentation device;
an interesting portion analyzer coupled to the first and second
content presentation devices for receiving a first input signal
from the first content presentation device indicative of a first
interesting portion of the plurality of portions of the digital
content identified by a first person included within a first social
network, and receiving a second input signal from the second
content presentation device indicative of a second interesting
portion of the plurality of portions of the digital content
identified by a second person included within a second social
network; a social network analyzer coupled to the first and second
content presentation devices and a third content presentation
device for determining if a third person operating the third
content presentation device is included in either the first social
network or the second social network; and a digital content preview
generator coupled to the third content presentation device for
generating a first digital content preview of the digital content
item including the first interesting portion and not the second
interesting portion based upon the third person being a member of
the first social network, and for generating a second digital
content preview of the digital content item including the second
interesting portion and not the first interesting portion based
upon the third person being a member of the second social
network.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0008] The accompanying figures wherein reference numerals refer to
identical or functionally similar elements throughout the separate
views, and which together with the detailed description below are
incorporated in and form part of the specification, serve to
further illustrate various embodiments and to explain various
principles and advantages all in accordance with the present
description, in which:
[0009] FIG. 1-FIG. 3 show various relationships between nodes or
member of a social network for social network analysis and for
content preview generation;
[0010] FIG. 4 shows a block diagram of a content distribution
system for generating a content preview using social network
analysis;
[0011] FIG. 5 shows an example flow diagram of a process for
generating a content preview using social network analysis;
[0012] FIG. 6 shows an example flow diagram for a process for
generating a content preview based upon a more identified
interesting portion within a social network;
[0013] FIG. 7 shows an example flow diagram for a process for
generating a content preview based upon a relationship magnitude
within a social network; and
[0014] FIG. 8 shows an example flow diagram for a process for
generating a content preview based upon a role magnitude within a
social network.
DETAILED DESCRIPTION
[0015] As required, detailed embodiments are disclosed herein;
however, it is to be understood that the disclosed embodiments are
merely examples and that the systems and methods described below
can be embodied in various forms. Therefore, specific structural
and functional details disclosed herein are not to be interpreted
as limiting, but merely as a basis for the claims and as a
representative basis for teaching one skilled in the art to
variously employ the present subject matter in virtually any
appropriately detailed structure and function. Further, the terms
and phrases used herein are not intended to be limiting, but
rather, to provide an understandable description of the
concepts.
[0016] The description of the present disclosure has been presented
for purposes of illustration and description, but is not intended
to be exhaustive or limited in the form disclosed. Many
modifications and variations will be apparent to those of ordinary
skill in the art without departing from the scope of the
description. The embodiment was chosen and described in order to
best explain the principles of the description and the practical
application, and to enable others of ordinary skill in the art to
understand the description for various embodiments with various
modifications as are suited to the particular use contemplated.
[0017] Content presentation devices include e-reading devices and
smartphones. Both e-reading devices and smartphones are rapidly
growing in popularity. An e-book reader, also called an e-book
device or e-reader, is a mobile electronic device that is designed
primarily for the purpose of reading digital e-books and
periodicals. Any device that can display text on a screen may act
as an e-book reader, but specialized e-book reader designs may
optimize portability, readability (especially in bright sun) and
battery life for this purpose. A single e-book holds the equivalent
of many printed texts with no added mass or bulk.
[0018] A smartphone is a mobile phone built on a mobile computing
platform with more advanced computing ability and connectivity than
a feature phone. The first smartphones mainly combined the
functions of a personal digital assistant (PDA) and a mobile phone
or camera phone. Today's models also serve to combine the functions
of portable media players, low-end compact digital cameras, pocket
video cameras, and GPS navigation units.
[0019] Targeted advertising is a type of advertising whereby
advertisements are placed so as to reach consumers based on various
traits such as demographics, psychographics, behavioral variables
(such as product purchase history), and firmographic variables . .
. or other second-order activities which serve as a proxy for these
consumer traits. Most targeted new media advertising currently uses
second-order proxies for targeting, such as tracking online or
mobile web activities of consumers, associating historical webpage
consumer demographics with new consumer web page access, using a
search word as the basis for implied interest, or contextual
advertising."
[0020] With the "Search Inside" feature of some on-line
booksellers, potential book buyers may see "sample pages" or
portions and links to different sections of the book, such as the
Front and Back Cover, Index, Table of Contents, and an Excerpt.
Users can sample or preview e-books. In one example, users can
download a sample of every e-book (usually first one or two
chapters) by pressing " sample" button on a web page previewing the
e-book. However, lookup, highlights and annotations don't work.
When reading on an e-book reader, designated highlights of
interesting portions are sent to a private page. Further,
highlights of all e-book customers and identify the passages with
the most highlights may be stored as Popular Highlights. The
resulting Popular Highlights help readers to focus on passages that
are meaningful to the greatest number of people.
[0021] A user's social network is analyzed to proactively and
selectively download portion of books or other documents to an
e-reader device and selectively display a preview of specific
portions of books or journal articles at a web page for buying or
otherwise acquiring such content. Because a significant number of
users in a social network (including clusters of individuals in a
company) have shared interests or jobs, a user is likely to be
exposed to useful segments of content and then potentially make a
purchase of the full content (e.g. a book or article).
[0022] FIG. 1 shows an example of users in a social network. An
analysis component (AC1) analyzes user's books (or articles) on an
e-reading device (e.g. AC1 scans e-reader for books recently read,
partially read, specific pages read, time spent on pages,
highlights made in books or articles, etc.). Then, analysis
component 2 (AC2) analyzes user's social network for other books
(or articles) read (in part or in their entirety) by members of
that network (e.g. a social network comprising email/twitter
contacts and the friends of email friends and clusters of company
thought leaders). If math-function-of (AC1 results, AC2 results,
n)>threshold, then proactively download portion of books (or
articles) discovered by AC2. (n may be the number of users in the
social network, and/or it can reflect how much of a book was read,
book reviews, distance in a social network, and other parameters).
If math-function-of (AC1 results, AC2 results, n)>threshold,
then display specific pages discovered by AC2 at a "Search Inside"
or "Preview" feature of a text-content seller's web site. (n may be
the number of users in the social network, and/or it can reflect
how much of a book was read, book reviews, distance in a social
network, and other parameters).
[0023] The system uses AC1 to form associations between a user of
the system and the books on the user's e-reader device, their
content, categories, or other descriptors. In addition, AC1 may
analyze the user and the books on the user's device in order to
categorize the user. AC2 forms associations between these
categories of user/e-reader content with members of the user's
social network and their e-reader content. In this way, a social
network may be represented as a set of relationships between users
of the system, with weights between nodes of this network
representing a relationship between users of e-readers and their
e-readers' content.
[0024] FIG. 1 shows a representation of the relationships and the
associations formed by AC1 and AC2. Note that User 1 and User 3 and
their e-readers' content are categorized differently by on AC1.
User 2 shares characteristics of each category (represented by the
AC1 association line). AC2 recognizes these relationships by
analyzing the existing social network and finding nodes that share
characteristics based on users and their e-readers' content. For
this reason, AC2 does not assign a connection between User 1 and
User 3, despite their being linked in the existing social
network.
[0025] The system may learn in the following manner. If a book or
article is proactively downloaded and then actually read, the
associations of users and content made by AC1 and AC2 are
strengthened relative to the book or article that is proactively
downloaded and not read. Thus, in future iterations of the
proactive download system, these "attractive" books or articles may
receive heavier weighting because they are more likely to be of
value. Note that a user's (or owner's) engagement with a book or
article (and/or the degree to which a book/article is "read") can
be estimated by many methods including: the amount of highlighting
a user has performed in an e-book, the number of pages turned, the
number of times an item is printed, the number of annotations made,
the amount of scrolling or "zooming" on a page, etc.). Also note
that a user may sometimes wish to avoid books that his or her
social network has little interest in. For example, a user may wish
to avoid books or articles that no one in his or her social network
has downloaded or read. In a similar manner, particular elements of
a book, magazine article, journal article, newspaper article, etc.
may be displayed in a "Search Inside," "Restricted Preview," or
related feature of an on-line seller of books, articles, etc. These
elements of a book or paper may include book pages, figures from a
technical journal article, equations from a technical journal
article, etc.
[0026] As shown in FIG. 2, association made by AC1 and AC2 may be
represented as a second graph or network, which is closely related
to the existing social network, wherein strengths of associations
between nodes of the graph correspond to the aforementioned
associations, which are strengthened when a book or article is
appropriately recommended or prospectively downloaded. The second
graph allows subsequent recommendations made by the system to
depend not only on which members of the user's social network has
read a particular book, and on the selectively strengthened weight
of association between this member (node) in the network and the
user, but also, in addition, relationships to nodes in this second
graph may correspond to finer representations of the role the
recommending individual plays in recommending books.
[0027] For example, an individual in FIG. 2 may be identified by
both "Larry-Automotive Repair" and "Larry-Fantasy Fiction"
relationships. These correspond to a user's friend Larry's
relatedness to the user's interests in 1) automotive repair books
and 2) fantasy fiction books. In this way, Larry's skill in
selecting useful automotive repair manuals may be exploited by
strengthening the weight of this link to the user, while his
somewhat different and less useful taste in fantasy fiction can be
represented by a decreased weight of connection. This is
represented in the modified graph of FIG. 2.
[0028] In one example, Joe is browsing to a page at an online or
journal seller such as a professional society's web site. Joe
wishes to preview certain elements of the online content. In this
example, preview pages, figures, equations, or other elements may
be revealed as determined by AC1 and AC2. In this manner, Joe is
likely to receive an interesting, popular, and/or useful segment of
the content, which may also encourage Joe to purchase or otherwise
acquire the online content.
[0029] In another example, if a user is a graphic designer working
at a company, it is likely that he or she has other graphic
designers or other like-minded people in his or her social network
who may have read certain technical books or articles. Social
networks include those formed through standard company tools such
as a company's listing of departments and groups or through general
social media networks such as Facebook, Twitter, Myspace, along
with gaming sites, virtual universe sites, etc. Also, email and
instant message contact lists, a record of emailing and messaging,
etc, may be mined for social network information.
[0030] As another example, Sue is browsing an on-line page related
to a technical journal paper published by a technical society.
Perhaps the paper is important to her for her technical work at her
company. She wishes to get a better idea about the paper before
making a purchase. The seller of papers reveals, for example, page
3, equation 2, Figure 3, and code snippet 2 to be interesting
portions--because AC1 and AC2 have determined that these are
popular items in Sue's social network and/or highlighted by any of
members of her: social network, company, department in a company,
etc. In this manner, Sue is likely to receive an interesting
segment of the content, which may also encourage her to purchase
the online content to help her at her job. Sue may also specify
specific members or classes of members of her social network or
company network. For example, perhaps she may follow a set of
distinguished engineers or managers who contribute to AC2. In
addition, the inventive system may discover by means of learning
that Sue values a colleague's recommendation for software design
manuals, since she always reads recommendations derived from this
association, but she does not value the colleagues recommendations
in travel magazines. The AC2 system therefore creates two
relationships in the second graph corresponding to this colleague's
roles, "Colleague1-software" and "Colleague1-travel" and applies
weights to these associations accordingly.
[0031] As yet another example, Sam is considering subscribing to a
certain newspaper but is hesitating because of the expense, or
because he is not sure if the newspaper interests him. The
newspaper, with Sam's permission, may display a customized version
based on the articles read by Sam's social network in order to
provide an incentive for him to subscribe. Members of his social
network may share some of Sam's interests. The newspaper could
optionally offer this customized version to Sam as his subscription
for a price that is different from the standard subscription
price.
[0032] Potential benefits of the system may be realized by readers
because they are exposed to potentially interesting new books and
articles and vendors/stores because their devices and services will
be "sticky" (i.e. be more attractive than devices and services
without this feature). Authors and publishers benefit because more
readers are exposed to their content. Other potential benefits
include: proactive downloading of portions of books/articles,
analysis of degree to which a book/article is read, specific pages
read, highlights made in books, time spent on pages, etc. Another
potential benefit allows for the growing popularity of e-readers
and smart-phones, and the scanning of books/articles residing on an
e-reader device, etc. while emphasizing the exposure of potential
buyers to useful "previews" and segments at a web site used for
selling books, journal papers, etc.--where the precise nature of
the preview segment is determined by an analysis of a user's social
network.
[0033] Additionally, the AC2 may consider attributions that may be
mined and used with respect to Degree Centrality, Betweenness
Centrality, Closeness, Eigenvalue Hub, and Authority. As an
example, an "Authority" generally has a high number of
relationships "pointing to it" and acts as a knowledge source of
information. A Hub is an individual that points to a relatively
large number of Authorities. These characterizations of the social
network can be made known using known network-analysis tools. In
enterprise business scenarios, employee subject matter experts
(SMEs) may also be explicitly identified by individuals (e.g.
experts, managers, IT staff, etc) to act in one or more Centrality
Roles to support more effective analysis in this description.
[0034] Social network analysis may then allow targeted advertising
and marketing to proceed in a new way. Since individuals often
recommend books to one another in private, the analyses described
here is possible, which may make recommendations before an exchange
(e.g. an informal verbal exchange) takes place between users of the
system. In other words, since individuals often recommend books to
one another informally, for example through spoken conversation,
the market analyses externalizes these recommendations and in order
to generate them before an informal exchange takes place between
users of the system. Therefore, the notion that the secondary graph
described to represent reader recommendation relationships is in
fact a directed graph. "Source" nodes, i.e., individuals who are
more likely to find new things to read, independent of
recommendations of others, may be identified through network
analysis. Since these peoples introduce new books and material into
the network, their role as "hubs" may be exploited by targeted
advertising and other information related to new releases and
publications, possibly allowing promotional copies of certain books
to be provided to such hub individuals automatically. In this way,
the system can target marketing material differently to these
"hub/source nodes" than to other nodes in the graph which are more
likely to make consumer decisions about books based on the
recommendations of others.
[0035] The system described herein may also be used to "filter"
presentations of books and articles, for example, presentations at
a book-selling or article-selling website. For example, based on
the results of AC1 and AC2, when a user is browsing books at a
bookseller's website, the user may only see those books that are
read or downloaded by others in their social network. This
represents a customized view based on AC1 and AC2. Similarly, AC1
and AC2 may be used to change the ordering of books or articles
presented at a website. For example, those books downloaded the
most often by members of a social network may appear before those
that are seldom downloaded.
[0036] A trigger based on a threshold may depend on the detection
of a trend within a social network, within a company, within a
department of a company, etc. Once a trend is detected among
multiple users or consumers, the system may be triggered.
Bellwethers or various kinds of leaders may be used to enhance
trend prediction. When a behavior develops among a large
population, and then is followed with enthusiasm for some period T,
this may also be used as a trigger. Trend estimation (a statistical
method) can be used to construct a model, for example, to determine
if buying patterns (or download or reading patterns) exhibit an
increasing or decreasing trend that is statistically distinguished
from random behavior. These trends may be restricted to given
locations.
[0037] An example of how trends and triggers may depend on temporal
dynamics includes the analysis of downloading and reading of
e-reader book in time. In this way, AC2 may not only correlate
nodes in a network and their relationships to a user in order to
make recommendations for e-reader downloads, but may also correlate
the behavior of these nodes (social network/recommender roles) in
time, such that if many recommender relationships to the user
download and read the same book simultaneously, the recommendation
and download of the same e-book is much faster than if these same
recommender nodes download and read the same book sequentially.
This may prove especially useful in the event that the simultaneity
in these behaviors is due to a secondary, hidden cause, such as an
event in the workplace, a new release of a competitor's product,
etc., which is not represented in the existing secondary graph.
This secondary cause is represented in FIG. 3.
[0038] Note that this system has application beyond magazines,
newspapers, books, and related documents and subscriptions. For
example, the approach may be used to show previews of complex
virtual worlds and games. Also, it may also be used to actually
create a subset of a virtual world or game that is likely to
interest the user. The approach may also be used for creating
custom movie or TV show previews based on social networks, instead
of the more traditional "movie trailers."
[0039] For example, an analysis component (AC1) analyzes a user's
watched movies, TV shows, educational content, documentaries,
instructional videos, etc. (e.g. AC1 scans a multimedia device or
service provider system for movies recently watched, partially
watched, specific scenes voted on, time spent replaying certain
scenes, highlights made on scenes, etc.). Analysis component 2
(AC2) analyzes user's social network for other movies or broadcasts
watched (in part or in their entirety) (e.g. a social network
comprising email/twitter contacts and the friends of email friends
and clusters of company thought leaders). If math-function-of (AC1
results, AC2 results, n)>threshold, then proactively download
movies (or broadcasts) discovered by AC2. (n may be the number of
users in the social network, and/or it can reflect how much of a
movie was seen, movie reviews, distance in a social network, and
other parameters). If math-function-of (AC1 results, AC2 results,
n)>threshold, then generate a preview including scenes and
segments discovered by AC2 on a movie trailer or other form of
"Preview" feature at a movie rental web site, commercial
presentation, etc.
[0040] In a virtual world or game setting, the virtual world may
consist of numerous islands, buildings, lands, etc. However, only a
subset of this territory will be of high interest to a potential
user or player. Some territory will likely be of more interest if a
user's social network has made use of or suggested such
territories. For example, an analysis component (AC1) analyzes a
user's traversal in a virtual world or game, etc. (e.g. AC1 scans a
virtual world or game service provider system for
territories/buildings recently traversed, partially traversed,
specific scenes/buildings/territories voted on, time spent in
territories and buildings, votes made with respect to
scenes/buildings/territories, etc.). Analysis component 2 (AC2)
analyzes user's social network for other virtual world or game
territories traversed (in part or in their entirety) (e.g. a social
network comprising email/twitter contacts and the friends of email
friends and clusters of company thought leaders). The social
network may also include players or avatars encountered before in
the past while in a virtual world or game. If math-function-of (AC1
results, AC2 results, n)>threshold, then generate a preview
including a portion of a 3-D landscape, building, or territory
discovered by AC2. (n may be the number of users in the social
network, and/or it can reflect how much of a movie was seen, movie
reviews, distance in a social network, and other parameters). If
math-function-of (AC1 results, AC2 results, n)>threshold, then
generate a preview including specific scenes, buildings, and
territories discovered by AC2 on a promotional trailer or other
form of "Preview" feature at a virtual world or game site.
[0041] In one example, a system comprises a first analysis
component (AC1) that analyzes a user's use and ownership of books
and articles, a second analysis component 2 (AC2) that analyzes a
user's social network for other books (or articles) read (in part
or in their entirety), a third analysis component (AC3) that
analyzes results from AC1 and AC2--based on trigger from AC3, a
preview generator that generates a preview having a portion of
other books/articles discovered by AC2, based on trigger from AC3,
then display specific pages (or elements on a page) discovered by
AC2 at a "Search Inside" or "Preview" feature of a text-content
seller's web site. The analysis of a user's text content restricted
to any of recently used/read, recently downloaded, recently
evaluated by the user and frequency of content use>N occurrences
per unit time. The text content used by individuals in the user's
social network are restricted to any of: recently used/read,
recently downloaded, recently evaluated by the user; frequency of
content use>N occurrences per unit time; an analysis based on
any of Degree Centrality, Betweenness Centrality, Closeness,
Eigenvalue Hub, and Authority; and physical location of readers
(e.g. readers who are geographically close within a company may be
able to provide hands-on help or advice). The AC3 analysis includes
the number of users (e.g. readers) in a social network who have
used/read a text content (e.g. book) with frequency>f. An icon
that represents a book or paper indicates the degree to which the
book or paper has certain usage or social-network attributes (e.g.
using color, font for title, size, shape, etc.). The icon may
appear on web sites of content sellers, and the icon and site may
be customized for individual users. The content may include virtual
worlds (landscapes, buildings etc.). AC2 and AC3 may provide for a
second graph, wherein nodes in a social network representing
individuals may connected by multiple relationships representing
the individual's one or more roles in recommending books to the
user.
[0042] What is described includes a system in which a distributor
of content (e.g. magazine, technical journal, or book content) can
provide a more useful "Search Inside" (e.g. "partial preview") for
such content at the point-of-sale Web page, and this preview is
more likely to be of value to potential buyers than approaches that
may, for example, only show a paper Abstract or a textbook first
chapter. More particularly, an analysis is performed of a user's
social network to determine useful preview content, such as a
paragraph, figure, formula, chapter, piece of code, etc. Because a
significant number of users in a social network (including clusters
of individuals in a company) have shared interests or jobs, in this
manner, a user is likely to be exposed to useful segments of
content and then potentially make a purchase of the full content
(e.g. a book or article). A graph is also described that can be
used to determine more nuanced previews of content, wherein
relationships to nodes (people) in a graph may correspond to finer
representations of people "roles" when recommending sections for
partial preview and for books. For example, an individual in the
graph representation may be identified by "Larry-Automotive
Repair," and "Larry-Fantasy Fiction." relationships.
[0043] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the description. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise.
[0044] The terms "comprises" and/or "comprising," specify the
presence of stated features, steps, operations, elements, and/or
components, but do not preclude the presence or addition of one or
more other features, integers, steps, operations, elements,
components, and/or groups thereof.
[0045] The term "digital content item" includes an electronically
stored volume such as a book, article, movie, television show,
game, and virtual world, and may include text information having
passages, multimedia information such as video and/or audio
information having multimedia portions such as scenes, and
interactive content having interactive portions which may be
selected in response to inputs received during presentations of
prior portions of interactive content, such as inputs received
while playing a video game or presenting a virtual world.
[0046] The term "plurality of portions" of the digital content
indicates that the digital content may be comprised of multiple
segments. For example, text information has passages including
chapters, paragraphs, sentences, phrases, and formulas, multimedia
information may have multimedia portions including scenes,
interactive content may have interactive portions including virtual
world locations or video game sequences.
[0047] The term "interesting portion" indicates one of the
plurality of portions that has been identified by a person who has
acquired the digital content item. A text based interesting portion
may be a passage that is highlight or bookmarked, have
interlineations, or time may have been spent reading the passage. A
multimedia interesting portion or an interactive interesting
portion may be replayed, voted upon or highlighted.
[0048] The term "content presentation device" indicates an
electronic device that presents a digital content item to a person.
A content presentation device may include an e-reader, e-book,
cellphone, tablet, personal computer, gaming device or other device
for presentation of a digital content item.
[0049] The term "digital content server" indicates a system for
distributing, purchasing, renting, and/or previewing digital
content items. A digital content server may be included within
digital content distributions systems that allow for presentation
of text, multimedia and/or interactive content on content
presentation devices.
[0050] The term "social network" indicates a network where a person
has associations or relationships with other persons. The social
network perspective provides a set of methods for analyzing the
structure of whole social entities as well as a variety of theories
explaining the patterns observed in these structures, and may
include social networking services such as Facebook, Twitter,
LinkedIn Google+, and My Space.
[0051] The term "relationship magnitude" indicates a closeness of
association between members of a social network. Relationship
magnitude may be determined using social network analysis metrics
such as degree centrality, betweenness centrality, closeness,
Eigenvalue hub and authority, a family relationship, an employment
relationship, a social media relationship status, and an interest
in a topic related to the first content item.
[0052] The term "role magnitude" indicates a status of a member of
a social network. Role magnitude may be determined determining the
expertise of a social network member by determining the number of
digital content items having interesting portions identified by the
member within the subject matter of the digital content item. Also
role magnitude may indicate a hierarchical status of the member
within an organization related to social network. Such a
hierarchical status includes employer/employee, doctor/patient,
parent/child.
[0053] FIG. 4 shows a block diagram of a content distribution
system for generating a content preview using social network
analysis. Content distribution server 100 includes a content
distributor 102 having a digital memory 104 for storing digital
content items including digital content item 106 having a plurality
of portions 110-118. In one example, if digital content item 106
comprises text portions such as an e-book, then plurality of
portions 110-118 could correspond to paragraphs or other sections,
portion 110 could include the title, author and abstract while
portions 112-118 corresponding to paragraphs of the body of the
e-book.
[0054] The content distributor 102 distributes the digital content
item 106 to a first content presentation device 120 operated by a
first person 122 belonging to a first social network 124. Content
portion 118 has been identified as a first interesting portion by
the first person using the content presentation device 120, and in
response, interesting portion identification process 126 generates
a first input signal indicative of the first interesting portion
118.
[0055] The content distributor 102 also distributes the digital
content item 106 to a second content presentation device 130
operated by a second person 132 belonging to a second social
network 134. Content portion 114 has been identified as a second
interesting portion by the second person using the content
presentation device 130, and in response, interesting portion
identification process 136 generates a second input signal
indicative of the second interesting portion 114.
[0056] The first content presentation device 120 and the first
person 122 may act as a node 128 on the first social network 124.
In other examples, the first social network may include additional
nodes 128B-128C, each additional node having digital content item
106 and a person able to identify interesting portions wherein an
interesting portion process generates additional input signals
indicative of the identified interesting portion. In other
examples, other similar additional nodes may be added to node 138
and included in the second social network. In yet other examples
there may be more than two social networks.
[0057] Interesting portion analyzer 140 is coupled to the first and
second content presentation devices 120, 130 and receives the first
and second input signals from interesting portion processes 126 and
136.
[0058] Social network analyzer 140 is also coupled to the first and
second content presentation devices 120, 130 as well as a third
content presentation device 150 operated by a third person 152
included within the first social network 124, and a fourth content
presentation device 160 operated by a fourth person 162 included
within the second social network 134, and a fifth content
presentation device 170 operated by a fifth person 172 which is not
a member of either the first or second social networks 174. Social
network analyzer determines which content presentation devices are
operated by various persons within social networks. For example
social network analyzer determines that content presentation
devices 120 and 150 are operated by persons included within first
social network 124, content presentation devices 130 and 160 are
operated by persons included within the second social network 134,
and content presentation device 170 is operated by a person not
included in either the first or second social network.
[0059] Content previews 156, 166 and 176 are to be generated for
the first digital content item 106 on content presentation devices
150, 160 and 170 for persons 152, 162 and 172 by content preview
generator 180. All content previews 156, 166 and 176 are shown
having a common portion 110 which may correspond to a title,
abstract or other descriptive portion of the content. However, the
remaining portions of the content preview include portions that may
be based upon upon the social network of the person operating the
content presentation device. For any of a number of reasons,
persons 152, 162 and 172 may be previewing the first digital
content item 106, and after the preview may decide to acquire
access to the first digital content item. Content distribution
server 100 provides for the generation of three different previews
156, 166 and 176 by content preview generator 180 based upon the
social network status of persons 152, 162, and 172 as determined by
social network analyzer 140. In this example, person 152 and 122
are members of the first social network 124 and person 122 has
indicated that portion 118 is an interesting portion, consequently
content preview generator includes portion 118 in preview 156 for
person 152. Similarly, person 162 and person 132 are members of the
second social network 134 and person 132 has indicated that portion
114 is an interesting portion, consequently content preview
generator includes portion 114 in preview 166 for person 162.
However, person 172 is not a member of either the first social
network 124 or the second social network 134, consequently content
preview generator includes portion 112 in preview 176 for person
172. Portion 112 may be the first paragraph after abstract 110, or
may be a portion selected for preview by the author or publisher or
other person where the social network status of the person viewing
the preview is not taken into account.
[0060] Note further that preview 156 does not necessarily include
portion 114 and preview 166 does not necessarily include portion
118 because persons 152 and 162 are in different social networks.
Also, preview 176 does not necessarily include either portions 114
or 118 because person 172 is not included in either the first or
second social network.
[0061] In an example where another person included within a node of
the first social network identifies an interesting portion
different from portion 118, social network analyzer may further
analyze a relationship magnitude between the members of social
network 1 and generate a preview accordingly. For example, if
person 112 had a greater relationship magnitude with the previewer
than the person of node 128B, then portion 118 would be selected
for preview rather than the interesting portion identified by the
person of node 128B. Relationship magnitude may be determined using
social network analysis metrics such as degree centrality,
betweenness centrality, closeness, Eigenvalue hub and authority, a
family relationship, an employment relationship, a social media
relationship status, and an interest in a topic related to the
first content item.
[0062] In an example where another person included within a node of
the first social network identifies an interesting portion
different from portion 118, social network analyzer may further
analyze a role magnitude between the members of social network 1
and generate a preview accordingly. For example, if person 112 had
a greater role magnitude than the person of node 128B, then portion
118 would be selected for preview rather than the interesting
portion identified by the person of node 128B. Role magnitude may
be determined based upon the expertise or hierarchical status. For
example, if the person of node 120 was the supervisor of person
162, while the person of node 120B was a contemporary of person
162, then portion 118 would be selected because of the greater role
magnitude of the person of node 120. In another example, if the
person of node 120 had reviewed more digital content items within
the subject area of digital content item 106 and identified more
corresponding interesting portions than the person of node 120B, as
determined by distribution analyzer process 182, then portion 118
would be selected because of the greater role magnitude of the
person of node 120.
[0063] If a person decides to acquire the digital content item,
including the plurality of portions, an acquisition signal is
received and a content transaction is managed by content
transaction process 184. The acquisition may take the form of
no-compensation download, an advertised subsidized presentation, a
limited term rental, a purchase or other type of transaction used
to acquire content. The acquired content may be presented on any
device operated by the acquiring person including the content
presentation device receiving the preview.
[0064] A person identifying interesting portions of acquired
content may be further provided additional information related to
new content by new content information distributor 186. The new
content may be included in a subject area associated with content
for which interesting portions have been identified by the person.
In this way, the person may provide further identification of
interesting portions of new content to help further facilitate the
acquisition of the new content by previews generated for members of
the social network of the person. The new content information may
further be conditioned upon the person identifying interesting
content of a digital content item that was used in a preview that
resulted in an acquisition of the digital content item by the
person viewing the preview.
[0065] FIG. 4 shows a system for enabling a process for identifying
a first interesting portion 118 of a digital content item 106
having a plurality of portions 110-118, the first interesting
portion identified based upon a first input signal by process 126
received at a first content presentation device 120, the first
content presentation device operated by a first person 122
associated with a first social network 124. The process further for
identifying a second interesting portion 114 of the digital content
item 106, the second interesting portion identified based upon a
second input signal by process 136 received at a second content
presentation device 130, the second content presentation device
operated by a second person 132 associated with a second social
network 134. It is then determined that a third person 152
operating a third content presentation device 150 is associated
with the first social network 124 and a fourth person 162 operating
a fourth content presentation device 160 is associated with the
second social network 134. In response the process generates a
first digital content preview 156 of the digital content item for
presentation on the third content presentation device, the first
digital content preview including the first interesting portion 118
based upon the third person being associated with the first social
network, a second digital content preview 166 of the digital
content item for presentation on the fourth content presentation
device, the second digital content preview including the second
interesting portion 114 based upon the fourth person being
associated with the second social network.
[0066] The process further allows for enabling presentation of the
first digital content preview by the third content presentation
device. The content transaction process 184 allows for receiving an
acquisition signal associated with the presentation of the first
digital content preview and enabling presentation of the plurality
of portions of the digital content item by a content presentation
device operated by the third person based upon the compensation
signal. New content information distributor 186 allows for
communicating information related to a second digital content item
to the first person based upon the acquisition signal.
[0067] The digital content item may include text information having
a plurality of passages corresponding to the plurality of portions
and the first interesting portion may correspond to at least a
first of the plurality of passages and the first input signal may
be based upon a first annotation to the text information received
from the first person at the first content presentation device. The
first annotation may include at least one of text highlights,
interlineations and bookmarks. In another example, the first input
signal is based upon an amount of time spent reading the first of
the plurality of passages by the first person at the first
presentation device.
[0068] In another example, the digital content item includes
multimedia information having a plurality of multimedia portions
corresponding to the plurality of portions and the first
interesting portion corresponds to at least a first of the
plurality of multimedia portions and the first input signal is
based upon at least one of replaying, voting and highlight made on
the first of the plurality of multimedia portions by the first
person at the first presentation device.
[0069] In another example, the digital content item includes a
plurality of interactive portions corresponding to the plurality of
portions, wherein presentation of a subsequent interactive portion
is based upon inputs received during presentation of a prior
interactive portion and the first interesting portion corresponds
to at least a first of the plurality of interactive portions and
the first input signal is based upon at least one of replaying,
voting and highlight made on the first of the plurality of
interactive portions by the first person at the first presentation
device.
[0070] FIG. 5 shows an example flow diagram of a process for
generating a content item preview using social network analysis.
Step 502 determines if an input signal is received that identifies
an interesting portion of content of a digital content item from a
person associated with a social network. If so, step 504 stores
identification of the interesting portion and associated social
network. Additional information may also be stored to allow for
determination of the relationship magnitude and the role magnitude.
Step 506 determines if a preview of the digital content item is to
be generated for another person. The preview may be generated in
response to any of a multiplicity of processes used to recommend a
preview to a person. If a preview is to be generated, step 508
determines if the other person belongs to a social network for
which at least one interesting portion has been identified. If not,
step 510 generates a predetermined preview of the digital content
item, as shown in preview 176 of FIG. 4. If the other person does
belong to a social network having an identified interesting
portion, then step 512 generates a preview of the digital content
using the interesting portion for presentation to the other person
as shown in preview 156 or 166 of FIG. 4. Then, step 514 determines
if an acquisition signal has been received from the other person in
response to the preview. If so, step 516 enables presentation of
all portions of the digital content item to the other person and
step 518 communicates information of a second digital content item
to a person identifying the interesting portion used for the
preview.
[0071] FIG. 6 shows an example flow diagram for a process for
generating a preview based upon a more identified interesting
portion within a social network. The process begins at step 512. If
multiple input signals are received from persons within a social
network that identify multiple interesting portions of the digital
content item at step 602, then step 604 identifies a more
identified interesting portion to include within a preview. The
more identified portion may correspond to a most identified
portion, or a highly identified portion combined with other factors
such as relationship magnitude and/or role magnitude for selecting
between other highly identified portions. In one example, to be
included within a preview a portion must also be identified by a
minimum number of persons within the social network. The minimum
number may be two. FIG. 6 provides for the first interesting
portion to be based upon a first plurality of input signals, the
first plurality of input signals including the first input signal,
identifying the first interesting portion, and received at a first
plurality of content presentation devices including the first
content presentation device, the first plurality of content
presentation devices operated by a first plurality of persons
including the first person, the first plurality of persons
associated with the first social network. The first plurality of
input signals are included within a first multiplicity of input
signals identifying a first multiplicity of interesting portions
including the first interesting portion, the first plurality of
content presentation devices are included with a first multiplicity
of content presentation devices, the first plurality of persons are
included with a first multiplicity of persons operating the first
multiplicity of content presentation devices, the first
multiplicity of persons being associated with the first social
network, and the identifying the first interesting portion further
includes determining the first plurality of input signals to
indicate that the first interesting portion is a more identified
portion of the first multiplicity of interesting portions
identified by the first multiplicity of input signals.
[0072] FIG. 7 shows an example flow diagram for a process for
generating a preview based upon a relationship magnitude within a
social network. The process begins at step 512. If multiple input
signals are received form persons within a social network that
identify multiple interesting portions of the digital content item
at step 702, then step 704 determines a relationship magnitude
between the persons identifying interesting portions and the person
for which the preview is generated and step 706 generates the
preview based upon the relationship magnitude, using the
interesting portion identified by social network members having a
greater relationship magnitude. Determining the relationship
magnitude may include evaluating at least one of a degree
centrality, betweenness centrality, closeness, Eigenvalue hub and
authority, a family relationship, an employment relationship, a
social media relationship status, and an interest in a topic
related to the digital content item.
[0073] FIG. 8 shows an example flow diagram for a process for
generating a preview based upon a role magnitude within a social
network. The process begins at step 512. If multiple input signals
are received from persons within a social network that identify
multiple interesting portions of the digital content item at step
802, then step 804 determines a role magnitude between the persons
identifying interesting portions and the person for which the
preview is generated and step 806 generates the preview based upon
the role magnitude, using the interesting portion identified by
social network members having a greater role magnitude.
[0074] Approaches for selecting an interesting portion for a
preview for person from a multiplicity of identified interesting
portions identified by members of a social network are shown in
FIG. 6-FIG. 8. It should be appreciated that any combination of
"more identified", "relationship magnitude", and "role magnitude"
selection criterion may be utilized in the selection of an
interesting portion for preview while remaining within the scope of
this description. For example, if two interesting portions are
similarly "more identified" and the subject matter deals with
entertainment, then "relationship magnitude" may be utilized to
select between the two portions, particularly if the relationships
are friend or family relationships. If two interesting passages are
similarly "more identified" and the subject matter deals with items
of a professional nature, then "role magnitude" may be utilized to
select between the two portions, particularly if the relationships
are employment relationships.
[0075] The present description may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present description.
[0076] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0077] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0078] Computer readable program instructions for carrying out
operations of the present description may be assembler
instructions, instruction-set-architecture (ISA) instructions,
machine instructions, machine dependent instructions, microcode,
firmware instructions, state-setting data, or either source code or
object code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present description.
[0079] Aspects of the present description are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the description. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0080] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0081] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0082] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present description. In
this regard, each block in the flowchart or block diagrams may
represent a module, segment, or portion of instructions, which
comprises one or more executable instructions for implementing the
specified logical function(s). In some alternative implementations,
the functions noted in the block may occur out of the order noted
in the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0083] The description of the present application has been
presented for purposes of illustration and description, but is not
intended to be exhaustive or limited to the description in the form
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
of the description. The example was chosen and described in order
to best explain the principles of the description and the practical
application, and to enable others of ordinary skill in the art to
understand the description for various examples with various
modifications as are suited to the particular use contemplated.
* * * * *