U.S. patent application number 16/414428 was filed with the patent office on 2020-05-07 for systems and methods for providing personalized content.
The applicant listed for this patent is Facebook, Inc.. Invention is credited to Thomas Frederick Dimson, Linji Yang, Chen Zheng.
Application Number | 20200145505 16/414428 |
Document ID | / |
Family ID | 61969953 |
Filed Date | 2020-05-07 |
View All Diagrams
United States Patent
Application |
20200145505 |
Kind Code |
A1 |
Zheng; Chen ; et
al. |
May 7, 2020 |
SYSTEMS AND METHODS FOR PROVIDING PERSONALIZED CONTENT
Abstract
Systems, methods, and non-transitory computer-readable media can
generate a set of candidate content items from a plurality of
content items that are available in the social networking system,
wherein one or more of the candidate content items are to be
included in a personalized content stream for a first user. A
corresponding score for each of the candidate content items can be
generated with respect to the first user. A first set of content
items can be determined from the set of candidate content items
based at least in part on the respective scores, wherein content
items in the first set are included in the personalized content
stream.
Inventors: |
Zheng; Chen; (Cupertino,
CA) ; Dimson; Thomas Frederick; (San Francisco,
CA) ; Yang; Linji; (Lafayette, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Facebook, Inc. |
Menlo Park |
CA |
US |
|
|
Family ID: |
61969953 |
Appl. No.: |
16/414428 |
Filed: |
May 16, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15299035 |
Oct 20, 2016 |
10320927 |
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16414428 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/18 20130101;
G06N 20/00 20190101; G06Q 50/01 20130101; G06Q 10/10 20130101; G06Q
30/0241 20130101; H04L 51/10 20130101; H04L 67/06 20130101; H04L
67/22 20130101; H04L 65/4084 20130101; H04L 51/32 20130101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; G06Q 30/02 20060101 G06Q030/02; G06Q 10/10 20060101
G06Q010/10; G06Q 50/00 20060101 G06Q050/00; H04L 29/06 20060101
H04L029/06; H04L 12/58 20060101 H04L012/58 |
Claims
1. A computer-implemented method comprising: generating, by a
social networking system, a set of candidate content items from a
plurality of content items that are available in the social
networking system, wherein one or more of the candidate content
items are to be included in a personalized content stream for a
first user; generating, by the social networking system, a
corresponding score for each of the candidate content items with
respect to the first user; and determining, by the social
networking system, a first set of content items from the set of
candidate content items based at least in part on the respective
scores, wherein content items in the first set are included in the
personalized content stream.
2. The computer-implemented method of claim 1, wherein generating a
respective score for a candidate content item further comprises:
determining, by the social networking system, a likelihood of the
first user selecting an option to like a candidate content item
through the social networking system, the likelihood being
determined using a trained machine learning model, wherein the
score for the candidate content item is based at least in part on
the likelihood.
3. The computer-implemented method of claim 1, wherein generating a
respective score for a candidate content item further comprises:
determining, by the social networking system, a likelihood of the
first user watching one or more additional content items after
having viewed a candidate content item, the likelihood being
determined using a trained machine learning model, wherein the
score for the candidate content item is based at least in part on
the likelihood.
4. The computer-implemented method of claim 1, wherein generating a
respective score for a candidate content item further comprises:
determining, by the social networking system, a likelihood of the
first user watching a candidate content item to completion, the
likelihood being determined using a trained machine learning model,
wherein the score for the candidate content item is based at least
in part on the likelihood.
5. The computer-implemented method of claim 1, wherein generating a
respective score for a candidate content item further comprises:
determining, by the social networking system, a likelihood of the
first user watching a playback of a candidate content item for a
duration of time that is longer than an average duration of time
the first user watches playback of content items, the likelihood
being determined using a trained machine learning model, wherein
the score for the candidate content item is based at least in part
on the likelihood.
6. The computer-implemented method of claim 1, wherein generating a
respective score for a candidate content item further comprises:
determining, by the social networking system, a likelihood of the
first user watching a playback of a candidate content item for a
duration of time that is longer than an average duration of time
that other users watched playbacks of the candidate content item,
the likelihood being determined using a trained machine learning
model, wherein the score for the candidate content item is based at
least in part on the likelihood.
7. The computer-implemented method of claim 1, wherein generating
the set of candidate content items further comprises: obtaining, by
the social networking system, one or more content items that were
liked by at least one second user that the first user is following
in the social networking system.
8. The computer-implemented method of claim 1, wherein generating
the set of candidate content items further comprises: determining,
by the social networking system, that the first user has previously
liked one or more content items that were posted by at least one
second user; and obtaining, by the social networking system, one or
more content items that were liked by the second user.
9. The computer-implemented method of claim 1, wherein generating
the set of candidate content items further comprises: obtaining, by
the social networking system, one or more content items that were
posted by users that are located in a geographic region in which
the first user is also located or has visited.
10. The computer-implemented method of claim 1, the method further
comprising: filtering, by the social networking system, the set of
candidate content items to exclude content items that are likely to
be flagged by users as being inappropriate or content items that
were posted by users that have previously been flagged as posters
of inappropriate content.
11. A system comprising: at least one processor; and a memory
storing instructions that, when executed by the at least one
processor, cause the system to perform: generating a set of
candidate content items from a plurality of content items that are
available in the social networking system, wherein one or more of
the candidate content items are to be included in a personalized
content stream for a first user; generating a corresponding score
for each of the candidate content items with respect to the first
user; and determining a first set of content items from the set of
candidate content items based at least in part on the respective
scores, wherein content items in the first set are included in the
personalized content stream.
12. The system of claim 11, wherein generating a respective score
for a candidate content item further causes the system to perform:
determining a likelihood of the first user selecting an option to
like a candidate content item through the social networking system,
the likelihood being determined using a trained machine learning
model, wherein the score for the candidate content item is based at
least in part on the likelihood.
13. The system of claim 11, wherein generating a respective score
for a candidate content item further causes the system to perform:
determining a likelihood of the first user watching one or more
additional content items after having viewed a candidate content
item, the likelihood being determined using a trained machine
learning model, wherein the score for the candidate content item is
based at least in part on the likelihood.
14. The system of claim 11, wherein generating a respective score
for a candidate content item further causes the system to perform:
determining a likelihood of the first user watching a candidate
content item to completion, the likelihood being determined using a
trained machine learning model, wherein the score for the candidate
content item is based at least in part on the likelihood.
15. The system of claim 11, wherein generating a respective score
for a candidate content item further causes the system to perform:
determining a likelihood of the first user watching a playback of a
candidate content item for a duration of time that is longer than
an average duration of time the first user watches playback of
content items, the likelihood being determined using a trained
machine learning model, wherein the score for the candidate content
item is based at least in part on the likelihood.
16. A non-transitory computer-readable storage medium including
instructions that, when executed by at least one processor of a
computing system, cause the computing system to perform a method
comprising: generating a set of candidate content items from a
plurality of content items that are available in the social
networking system, wherein one or more of the candidate content
items are to be included in a personalized content stream for a
first user; generating a corresponding score for each of the
candidate content items with respect to the first user; and
determining a first set of content items from the set of candidate
content items based at least in part on the respective scores,
wherein content items in the first set are included in the
personalized content stream.
17. The non-transitory computer-readable storage medium of claim
16, wherein generating a respective score for a candidate content
item further causes the computing system to perform: determining a
likelihood of the first user selecting an option to like a
candidate content item through the social networking system, the
likelihood being determined using a trained machine learning model,
wherein the score for the candidate content item is based at least
in part on the likelihood.
18. The non-transitory computer-readable storage medium of claim
16, wherein generating a respective score for a candidate content
item further causes the computing system to perform: determining a
likelihood of the first user watching one or more additional
content items after having viewed a candidate content item, the
likelihood being determined using a trained machine learning model,
wherein the score for the candidate content item is based at least
in part on the likelihood.
19. The non-transitory computer-readable storage medium of claim
16, wherein generating a respective score for a candidate content
item further causes the computing system to perform: determining a
likelihood of the first user watching a candidate content item to
completion, the likelihood being determined using a trained machine
learning model, wherein the score for the candidate content item is
based at least in part on the likelihood.
20. The non-transitory computer-readable storage medium of claim
16, wherein generating a respective score for a candidate content
item further causes the computing system to perform: determining a
likelihood of the first user watching a playback of a candidate
content item for a duration of time that is longer than an average
duration of time the first user watches playback of content items,
the likelihood being determined using a trained machine learning
model, wherein the score for the candidate content item is based at
least in part on the likelihood.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/299,035, filed on Oct. 20, 2016 and
entitled "SYSTEMS AND METHODS FOR PROVIDING PERSONALIZED CONTENT",
which is incorporated herein by reference in its entirety.
FIELD OF THE INVENTION
[0002] The present technology relates to the field of content
provision. More particularly, the present technology relates to
techniques for providing personalized content to users.
BACKGROUND
[0003] Today, people often utilize computing devices (or systems)
for a wide variety of purposes. Users can use their computing
devices to, for example, interact with one another, access content,
share content, and create content. In some cases, content items can
include postings from members of a social network. The postings may
include text and media content items, such as images, videos, and
audio. The postings may be published to the social network for
consumption by others.
[0004] Under conventional approaches, users may post various
content items to the social networking system. In general, content
items posted by a first user can be included in the respective
content feeds of other users of the social networking system, for
example, that have "followed" the first user. By following (or
subscribing to) the first user, some or all content that is
produced, or posted, by the first user may be included in the
respective content feeds of the following users. A user following
the first user can simply unfollow the first user to prevent new
content that is produced by the first user from being included in
the following user's content feed.
SUMMARY
[0005] Various embodiments of the present disclosure can include
systems, methods, and non-transitory computer readable media
configured to generate a set of candidate content items from a
plurality of content items that are available in the social
networking system, wherein one or more of the candidate content
items are to be included in a personalized content stream for a
first user. A corresponding score for each of the candidate content
items can be generated with respect to the first user. A first set
of content items can be determined from the set of candidate
content items based at least in part on the respective scores,
wherein content items in the first set are included in the
personalized content stream.
[0006] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a likelihood of the first user selecting an option to like a
candidate content item through the social networking system, the
likelihood being determined using a trained machine learning model,
wherein the score for the candidate content item is based at least
in part on the likelihood.
[0007] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a likelihood of the first user watching one or more additional
content items after having viewed a candidate content item, the
likelihood being determined using a trained machine learning model,
wherein the score for the candidate content item is based at least
in part on the likelihood.
[0008] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a likelihood of the first user watching a candidate content item to
completion, the likelihood being determined using a trained machine
learning model, wherein the score for the candidate content item is
based at least in part on the likelihood.
[0009] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a likelihood of the first user watching a playback of a candidate
content item for a duration of time that is longer than an average
duration of time the first user watches playbacks of content items,
the likelihood being determined using a trained machine learning
model, wherein the score for the candidate content item is based at
least in part on the likelihood.
[0010] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a likelihood of the first user watching a playback of a candidate
content item for a duration of time that is longer than an average
duration of time that other users watched playbacks of the
candidate content item, the likelihood being determined using a
trained machine learning model, wherein the score for the candidate
content item is based at least in part on the likelihood.
[0011] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to obtain one
or more content items that were liked by at least one second user
that the first user is following in the social networking
system.
[0012] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
that the first user has previously liked one or more content items
that were posted by at least one second user and obtain one or more
content items that were liked by the second user.
[0013] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to obtain one
or more content items that were posted by users that are located in
a geographic region in which the first user is also located or has
visited.
[0014] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to filter the
set of candidate content items to exclude content items that are
likely to be flagged by users as being inappropriate or content
items that were posted by users that have previously been flagged
as posters of inappropriate content.
[0015] Various embodiments of the present disclosure can include
systems, methods, and non-transitory computer readable media
configured to obtain information describing a personalized content
stream of a first user, the personalized content stream including a
set of content items to be presented to the first user according to
a first ordering. A second ordering for the set of content items is
determined based on one or more criteria, the second ordering
satisfying at least one measure of consistency. The personalized
content stream is modified to correspond to the second
ordering.
[0016] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to cluster
content items in the set based on one or more criteria, wherein
each content item is assigned to a cluster in a plurality of
clusters and determine an order in which to present each cluster in
the plurality of clusters.
[0017] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a respective classification for each content item in the set and
assign each content item in the set to a cluster in the plurality
of clusters based on its respective classification.
[0018] In some embodiments, the classification of a content item is
based on its assigned topic, category, sub-category, subject matter
classification, or visual theme.
[0019] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a respective geographic location for each content item in the set,
the geographic location corresponding to a geographic location of a
user that posted the content item and assign each content item in
the set to a cluster in the plurality of clusters based on its
respective geographic location.
[0020] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
a respective sound characteristics for each content item in the set
and assign each content item in the set to a cluster in the
plurality of clusters based on its respective sound
characteristics.
[0021] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
respective attributes describing music that is played during
playback of each content item in the set and assign each content
item in the set to a cluster in the plurality of clusters based on
the respective attributes describing the music played during
playback of the content item.
[0022] In some embodiments, the systems, methods, and
non-transitory computer readable media are configured to determine
respective distance scores between clusters in the plurality of
clusters, wherein a distance score for a first cluster and a second
cluster measures a similarity between the first cluster and the
second cluster and generate the order of clusters in the plurality
of clusters based at least in part on the respective distance
scores.
[0023] In some embodiments, the distance score for the first
cluster and the second cluster is determined based at least in part
on a visual similarity between content items in the first cluster
and content items in the second cluster.
[0024] In some embodiments, the distance score for the first
cluster and the second cluster is determined based at least in part
on a social affinity between users that posted content items in the
first cluster and users that posted content items in the second
cluster.
[0025] It should be appreciated that many other features,
applications, embodiments, and/or variations of the disclosed
technology will be apparent from the accompanying drawings and from
the following detailed description. Additional and/or alternative
implementations of the structures, systems, non-transitory computer
readable media, and methods described herein can be employed
without departing from the principles of the disclosed
technology.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] FIG. 1 illustrates an example system including an example
content provider module, according to an embodiment of the present
disclosure.
[0027] FIG. 2 illustrates an example personalized content stream
module, according to an embodiment of the present disclosure.
[0028] FIGS. 3A-B illustrates an example interface, according to an
embodiment of the present disclosure.
[0029] FIGS. 4A-C illustrate other example interfaces, according to
an embodiment of the present disclosure.
[0030] FIG. 5 illustrates an example method for providing
personalized content, according to an embodiment of the present
disclosure.
[0031] FIG. 6 illustrates an example method for reordering a
personalized content stream, according to an embodiment of the
present disclosure.
[0032] FIG. 7 illustrates a network diagram of an example system
including an example social networking system that can be utilized
in various scenarios, according to an embodiment of the present
disclosure.
[0033] FIG. 8 illustrates an example of a computer system or
computing device that can be utilized in various scenarios,
according to an embodiment of the present disclosure.
[0034] The figures depict various embodiments of the disclosed
technology for purposes of illustration only, wherein the figures
use like reference numerals to identify like elements. One skilled
in the art will readily recognize from the following discussion
that alternative embodiments of the structures and methods
illustrated in the figures can be employed without departing from
the principles of the disclosed technology described herein.
DETAILED DESCRIPTION
Approaches for Providing Personalized Content
[0035] People often utilize computing devices (or systems) for a
wide variety of purposes. Users can use their computing devices to,
for example, interact with one another, access content, share
content, and create content. In some cases, content items can
include postings from members of a social network. The postings may
include text and media content items, such as images, videos, and
audio. The postings may be published to the social network for
consumption by others.
[0036] Under conventional approaches, users may post various
content items to the social networking system. In general, content
items posted by a first user can be included in the respective
content feeds of other users of the social networking system that
have "followed" the first user. By following (or subscribing to)
the first user, some or all content that is produced, or posted, by
the first user may be included in the respective content feeds of
the users following the first user. A user following the first user
can prevent new content from the first user from being included in
the user's content feed by simply "unfollowing" the first user.
Under conventional approaches, there may be instances when a user
does not follow enough users to result in a desired amount of new
content to be included in the user's content feed. In one example,
the user may follow a limited number of other users that post new
content items infrequently. In this example, the user may be left
with a stale content feed once the content items posted by that
limited number of users have been exhausted, or viewed, by the
user. As a result, the user's continued engagement with the social
networking system may be negatively affected.
[0037] An improved approach rooted in computer technology overcomes
the foregoing and other disadvantages associated with conventional
approaches specifically arising in the realm of computer
technology. In various embodiments, users of the social networking
system can access content streams that have been personalized for
the user. Such content streams may include various types of content
items that each have been determined to be relevant, or of
interest, to a given user. In general, a personalized content
stream can be composed using a number of individual content items
that have been posted by various users of the social networking
system. In various embodiments, the personalized content stream for
a user can be continually updated to include newly posted content
items that have been determined to be relevant to the user. As a
result, the personalized content stream can provide an continuous
stream of relevant various content items that are available for the
user to browse. In some embodiments, such personalized content
stream can further be customized to improve the user experience.
For example, the presentation of content items as part of the
personalized content stream may be reordered based on topic, theme
(e.g., visual theme, audio theme, etc.), motion, geographic
location, and sound, to name some examples.
[0038] FIG. 1 illustrates an example system 100 including an
example content provider module 102, according to an embodiment of
the present disclosure. As shown in the example of FIG. 1, the
content provider module 102 can include a content module 104, a
follow module 106, a like module 108, and a personalized content
stream module 110. In some instances, the example system 100 can
include at least one data store 112. The components (e.g., modules,
elements, etc.) shown in this figure and all figures herein are
exemplary only, and other implementations may include additional,
fewer, integrated, or different components. Some components may not
be shown so as not to obscure relevant details.
[0039] In some embodiments, the content provider module 102 can be
implemented, in part or in whole, as software, hardware, or any
combination thereof. In general, a module as discussed herein can
be associated with software, hardware, or any combination thereof.
In some implementations, one or more functions, tasks, and/or
operations of modules can be carried out or performed by software
routines, software processes, hardware, and/or any combination
thereof. In some cases, the content provider module 102 can be
implemented, in part or in whole, as software running on one or
more computing devices or systems, such as on a user or client
computing device. In one example, the content provider module 102
or at least a portion thereof can be implemented as or within an
application (e.g., app), a program, or an applet, etc., running on
a user computing device or a client computing system, such as the
user device 710 of FIG. 7. In another example, the content provider
module 102 or at least a portion thereof can be implemented using
one or more computing devices or systems that include one or more
servers, such as network servers or cloud servers. In some
instances, the content provider module 102 can, in part or in
whole, be implemented within or configured to operate in
conjunction with a social networking system (or service), such as
the social networking system 730 of FIG. 7.
[0040] The content provider module 102 can be configured to
communicate and/or operate with the at least one data store 112, as
shown in the example system 100. The at least one data store 112
can be configured to store and maintain various types of data. For
example, the data store 112 can store information describing
various content that has been posted by users of a social
networking system. In some implementations, the at least one data
store 112 can store information associated with the social
networking system (e.g., the social networking system 730 of FIG.
7). The information associated with the social networking system
can include data about users, social connections, social
interactions, locations, geo-fenced areas, maps, places, events,
pages, groups, posts, communications, content, feeds, account
settings, privacy settings, a social graph, and various other types
of data. In some implementations, the at least one data store 112
can store information associated with users, such as user
identifiers, user information, profile information, user specified
settings, content produced or posted by users, and various other
types of user data.
[0041] The content provider module 102 can be configured to provide
users with access to content that is posted through a social
networking system. For example, the content module 104 can provide
a first user with access to content items through an interface that
is provided by a software application (e.g., a social networking
application) running on a computing device of the first user. The
first user can also interact with the interface to post content
items to the social networking system. Such content items may
include text, images, audio, and videos, for example.
[0042] In various embodiments, other users of the social networking
system can access content items posted by the first user. In one
example, the other users can access the content items by searching
for the first user through the interface, for example, by user
name. In some instances, some users may want to see content items
posted by the first user in their respective content feed. To cause
content items posted by the first user to be included in their
respective content feed, a user can select an option through the
interface to subscribe to, or "follow", the first user. The follow
module 106 can process the user's request by identifying the user
as a follower of (or "friend" of) the first user in the social
networking system. As a result, some or all content items that are
posted by the first user can automatically be included in the
respective content feed of the user. If the user decides that they
no longer want to see content from the first user in their
respective content feed, the user can select an option through the
interface to "unfollow" the first user. As a result, the follow
module 106 can remove the association between the user and the
first user so that content items posted by the first user are no
longer included in the content feed of the user. In some instances,
the user may want to endorse, or "like", a content item. In such
instances, the user can select an option provided in the interface
to like the desired content item. The like module 108 can determine
when a user likes a given content item and can store information
describing this relationship. In some embodiments, this information
can be stored in a social graph as described in reference to FIG.
7.
[0043] In various embodiments, the personalized content stream
module 110 is configured to generate customized content streams for
users using content items that are available from various sources
including any content items that are posted through the social
networking system. More details regarding the personalized content
stream module 110 will be provided below with reference to FIG.
2.
[0044] FIG. 2 illustrates a personalized content stream module 202,
according to an embodiment of the present disclosure. In some
embodiments, the personalized content stream module 110 of FIG. 1
can be implemented with the personalized content stream module 202.
As shown in the example of FIG. 2, the personalized content stream
module 202 can include a candidate generation module 204, a
filtering module 206, a ranking module 208, a continuity module
210, and a music module 212.
[0045] In various embodiments, the personalized content stream
module 202 can generate respective content streams for users. In
some embodiments, each personalized content stream is tailored for
a given user. That is, the personalized content stream will
generally include content items that have been determined to be of
interest to the user based, in part, on various metrics. As
mentioned, such personalized content streams may be composed using
various types of content items (e.g., animated content, videos,
etc.) that are posted, or otherwise available, through the social
networking system.
[0046] When generating a personalized content stream for a first
user, the candidate generation module 204 can determine a set of
candidate content items that are eligible for inclusion in the
personalized content stream. In some embodiments, the candidate
generation module 204 identifies as candidates any content items
that were liked by users that are being followed by the first user.
For example, if a second user that is being followed by the first
user likes a content item, then that content item can be included
in the set of candidate content items. In some embodiments, the
candidate generation module 204 identifies as candidates any
content items that were liked by users whose content items were
previously liked by the first user. For example, if the first user
liked a first content item that was posted by a second user and the
second user likes a second content item, then the second content
item can be included in the set of candidate content items. In some
embodiments, the candidate generation module 204 identifies as
candidates any content items that were posted by users that were
referenced in one or more search queries that were submitted by the
first user to the social networking system. For example, the first
user may have submitted search queries that reference a second user
to view the second user's profile and/or content items that have
been posted by the second user. In this example, any content items
posted by the second user can be included in the set of candidate
content items. In some embodiments, the candidate generation module
204 identifies as candidates any content items that were posted by
users that were referenced in one or more search queries that were
submitted by a threshold number of users of the social networking
system.
[0047] In some embodiments, the candidate generation module 204
identifies as candidates any content items that were posted by
users located in a geographic region (e.g., a point of interest,
city, zip code, state, country, continent, geofence, etc.) in which
the first user is also located and/or has visited in the past. Such
location information may be obtained, for example, from computing
devices of users that are used to access the social networking
system, metadata corresponding to content items that were posted by
users, and/or information provided by users in their respective
social profiles in the social networking system, to name some
examples. In some embodiments, the candidate generation module 204
identifies as candidates any content items that were posted by
users that are located in a geographic region in which users that
are followed by the first user (e.g., friends of the first user)
are located. For example, a content item posted by a third user can
be included in the set of candidates if the third user is located
in the same geographic region as a second user that is followed by
the first user.
[0048] In some embodiments, the candidate generation module 204
generates a new set of candidate content items when one or more
criteria is satisfied. For example, the candidate generation module
204 can generate a new set of candidate content items at
predetermined time intervals or after the first user has accessed,
or viewed, a threshold number (e.g., 10 content items) of content
items in the personalized content stream. In some embodiments, when
generating a new set of candidate content items for a user, the
candidate generation module 204 may discard content items included
in the previous set of candidate content items that were determined
for the user. In some embodiments, content items included in the
previous set of candidate content items are evaluated with respect
to any new candidate content items to identify the best scoring
content items to be included in the user's personalized content
stream.
[0049] The filtering module 206 can be configured to refine the set
of candidate content items by removing any content items that
satisfy certain filtering criteria. For example, in some
embodiments, content items included in the set may be restricted to
a certain type of content item (e.g., videos). In such embodiments,
any content items that do not match this type (e.g., images) are
removed from the set of candidate content items. In some
embodiments, a machine learning model can be trained to predict
whether a content item should be removed from the set of candidate
content items. In some embodiments, the model can be trained using
training examples that each reference content items that have been
hand labeled by quality control personnel as bad content that
should be excluded. Once trained, the model can then predict
whether a given content item should be excluded from the set of
candidate content items, for example, based on the subject matter
reflected in the content item. For example, a content item may be
excluded if the content item includes objectionable content (e.g.,
nudity, violence, etc.) or the content item is likely to be
reported as being inappropriate. In another example, a content item
may be excluded if a threshold number of users have skipped viewing
the content item and/or have reported the content item as being
inappropriate. A content item can also be excluded if the content
item was posted from a user account that has previously been
determined to post bad content, for example.
[0050] In some embodiments, the model can be trained using feedback
collected from users during their interactions with content items
in the social networking system. For example, content items may be
classified into one or more categories (e.g., games, news, comedy,
film, travel, sports, music, etc.) and/or sub-categories using
generally known content classification techniques (e.g., subject
matter classification). In such embodiments, user feedback (e.g.,
likes, dislikes or skips, etc.) for content items can be collected
and used to train the model to predict likelihoods of a user
"liking" a content item, of the user skipping playback of the
content item, and/or of the user discontinuing the viewing of a
personalized content stream in which the content item is included.
The filtering module 206 may exclude a content item if any of these
likelihoods satisfy a threshold value.
[0051] After filtering, the remaining content items in the set of
candidate content items can be scored and ranked for presentation
in the personalized content stream. In various embodiments, the
ranking module 208 can score each content item in the set with
respect to the first user using one or more trained machine
learning models. In some embodiments, a model can be trained to
predict a likelihood that the first user will "like" a given
content item. For example, the model can be trained using feedback
collected from the first user with respect to various content items
and their respective classifications, as described above. In some
embodiments, the software application running on the first user's
computing device through which the social networking system is
accessed may be configured to send information describing which
content items the first user has viewed, a respective view duration
for each content item, and/or a sequence in which the content items
were viewed, to name some examples. Such information can be used to
further train the model as described below.
[0052] For example, in some embodiments, the model can be trained
to predict a likelihood that the first user will continue to watch
more content items included in the personalized content stream
after viewing a given content item. In such embodiments, the
training examples used to train the model can each reference a
content item viewed by the first user and indicate whether the
first user continued watching a threshold number of content items
that were subsequently presented to the first user in the
personalized content stream. In some embodiments, the model can be
trained to predict a likelihood that the first user will watch a
given content item to completion. In such embodiments, the training
examples used to train the model can each reference a type and/or
classification for a content item that was presented to the first
user and indicate whether the first user watched the content item
to completion. Depending on the implementation, a content item may
be deemed as being watched to completion if the first user views
the entire duration of the content item or views the content item
for some threshold period of time (e.g., 3 seconds). In some
embodiments, a view count associated with the first user is
incremented after the first user has watched a content item to
completion. In some embodiments, the first user may be associated
with multiple view counts that each correspond to a particular type
and/or classification of content item. In such embodiments, the
view count incremented after the first user watches a content item
to completion corresponds to the type and/or classification of the
content item.
[0053] In some embodiments, the model can be trained to predict a
likelihood that the first user will watch the playback of a given
content item for a duration that is longer than an average duration
the first user typically views content items (e.g., before
discontinuing the playback, closing the software application,
etc.). In such embodiments, the training examples used to train the
model can each reference a type and/or classification for a content
item that was presented to the first user and indicate whether the
first user watched the content item for a duration that is longer
than the average duration. In some embodiments, the model can be
trained to predict a likelihood that the user will watch the
playback of a given content item for a duration that is longer than
an average duration that other users viewed the content item.
[0054] In various embodiments, a content item can be scored with
respect to the first user using any one of the approaches described
above or any combination thereof. When using multiple approaches to
score a content item, the respective likelihoods that measure the
first user's behavior can be combined (e.g., summed, multiplied,
etc.) to produce an overall score for the content item. In some
embodiments, the respective likelihoods may be weighted
differently, for example, by assigning respective coefficients to
the likelihoods.
[0055] In some embodiments, the model(s) can be trained to output
likelihoods that are specific for a given user. That is, a model
can output a likelihood that was predicted for a given user based
on feedback and/or information corresponding to that user. In some
embodiments, users are classified into one or more groups and the
model(s) are trained to output likelihoods that are group-specific.
That is, a model can output a likelihood that was predicted for a
given user based on feedback and/or information corresponding to a
group of users to which the given user was assigned. In one
example, users that have exhibited similar patterns of interactions
(e.g., users that follow similar users, users that like similar
content items, etc.) in the social networking system may be
included in the same group. In another example, users may be
grouped together based on their age range, gender, life stage
(e.g., user is enrolled in high school, user is enrolled in a
university, user is at some stage of their career, user is retired,
etc.), shared attributes among some proportion of users followed by
the user (e.g., users that tend to follow athletes, etc.),
location, language preference, to name some examples.
[0056] Once content items in the set of candidate content items
have been scored, the ranking module 208 can rank the content items
based on their respective scores. In some embodiments, the ranking
module 208 selects a threshold number (e.g., 20) of the top scoring
content items to be included in the personalized content stream for
the first user. This personalized content stream may be accessible
to the user through an interface as described below in reference to
FIGS. 3 and 4A-C.
[0057] In general, the content items included in the personalized
content stream have been determined to be relevant to the first
user. However, in an effort to improve consistency between content
item transitions, in some embodiments, the continuity module 210 is
configured to modify the order in which content items in the
personalized content stream are presented. For example, the
continuity module 210 can cluster content items in the personalized
content stream using one or more criteria. In some embodiments,
content items having the same classification (e.g., topic,
category, sub-category, subject matter classification, visual
theme, etc.) can be included in the same cluster. In some
embodiments, content items that were posted by users located in the
same geographic region or within some threshold distance of one
another can be included in the same cluster. In some embodiments,
content items having similar sound characteristics (e.g., audio is
within a threshold volume range, decibel range, frequency range,
etc.) can be included in the same cluster. For example, content
items that are associated with audio that satisfies a threshold
sound level consistency can be included in the same cluster. In
some embodiments, content items having the same music and/or song
are included in the same cluster. In some embodiments, content
items having similar music (e.g., same genre) are included in the
same cluster. In some embodiments, content items having similar
motion characteristics (e.g., having frame rates within a threshold
range) can be included in the same cluster. In some embodiments, a
first content item and a second content item can be included in the
same cluster if a threshold number of people continue to watch
playback of the second content item after playback of the first
content item ends.
[0058] The continuity module 210 can then determine the order in
which the clusters of content items will be presented in the
personalized content stream. In some embodiments, the continuity
module 210 determines the order based on respective distance scores
between the clusters. A distance score between a first cluster and
a second cluster may be computed using any of the approaches
described herein or any combination thereof. In some embodiments,
when computing a distance score between a first cluster and a
second cluster, the continuity module 210 can measure respective
distances between one or more content items in the first cluster
with respect to one or more content items in the second cluster.
For example, a distance score between a first cluster and a second
cluster may be determined based on a visual similarity between
content items in the first cluster and content items in the second
cluster. In another example, a distance score between a first
cluster and a second cluster may be determined based on a language
similarity (e.g., language spoken during playback of the content
items, language corresponding to text shown during playback of the
content item, etc.) between content items in the first cluster and
content items in the second cluster. In another example, a distance
score between a first cluster and a second cluster may be
determined based on a social affinity between users that posted
content items in the first cluster and users that posted content
items in the second cluster. The social affinity may be determined
using a social graph that is managed by the social networking
system, for example. In such embodiments, content items posted by
users having a strong social affinity will be closer in
distance.
[0059] The continuity module 210 can order the clusters based on
the distance scores. For example, a distance score between a first
cluster and a second cluster may be lower than a distance score
between the first cluster and a third cluster. In this example, the
personalized stream can be ordered so content items in the second
cluster are presented after playback of the content items in the
first cluster.
[0060] In some embodiments, the music module 212 can be configured
to identify background music, or songs, to be played during
playback of the personalized content stream. For example, the music
module 212 can identify any songs that will be played during
playback of the content items included in the personalized content
stream. The music module 212 can then select one or more of the
identified songs to be played during playback of content items in
the personalized content stream. In some embodiments, the songs to
be played are selected randomly. In some embodiments, the songs to
be played may be determined based on whether the first user will
like the song(s). For example, these may be songs that were written
and/or performed by artists that are followed by the first user in
the social networking system. In another example, the songs to be
played may have been written and/or performed by artists that are
local to the geographic region in which the first user is located.
In some embodiments, the music module 212 can select one or more
songs to be played during playback of a cluster of content items.
For example, the music module 212 can identify any songs that will
be played during playback of the content items included in a
cluster. The music module 212 can then select one or more of the
identified songs to be played during playback of content items in
the cluster using any of the approaches described above. In some
embodiments, when a song is being played during playback of the
content items, all other sound associated with the content items is
muted. In some embodiments, sound associated with the content items
is played at a lower volume setting while the song being played
during playback of the content items is played at a higher volume
setting.
[0061] FIG. 3A illustrates an example 300 of an interface 304,
according to an embodiment of the present disclosure. In this
example, the interface 304 is presented through a display screen of
the computing device 302. Further, the interface 304 may be
provided through an application (e.g., a web browser, a social
networking application, messenger application, etc.) running on the
computing device 302 that is configured to interact with a social
networking system. The interface 304 includes a number of different
options for accessing content through the social networking system.
In some embodiments, the interface 304 includes a first region
through which a user operating the computing device 302 can access
a personalized content stream 306 (e.g., "Videos You Might Like").
In some embodiments, the personalized content stream 306 begins
playing automatically in the first region as soon as the interface
304 is displayed. The interface 304 can also include a second
region through which a grid 308 of content items (e.g., content
items 310) can be accessed. For example, the user can select any of
the content items in the grid 308 to access the corresponding
content item. In some embodiments, one or more different
personalized content streams 312 that were generated for the user
can be included as a selectable content item in the grid 308. In
some embodiments, upon selecting the option 306, the software
application can be configured to provide an immersive interface 352
to allow full screen playback of the content items in the
personalized content stream, as illustrated in the example of FIG.
3B.
[0062] FIG. 4A illustrates another example 400 of an interface 404,
according to an embodiment of the present disclosure. In this
example, the interface 404 is presented through a display screen of
a computing device 402. Further, the interface 404 may be provided
through an application (e.g., a web browser, a social networking
application, messenger application, etc.) running on the computing
device 402 that is configured to interact with a social networking
system. The interface 404 includes a first region that presents a
carousel 406 of content items that are available for playback. In
FIG. 4A, the carousel 406 is shown referencing the personalized
content stream 410. The carousel 406 can include both personalized
content streams as well as curated content items. The interface 404
can also include a second region through which a grid 408 of
content items can be accessed, as described above. The carousel 406
can cycle through different content items that are available for
selection as illustrated in FIG. 4B. In FIG. 4B, the carousel 406
is shown transitioning from referencing the personalized content
stream 410 to a curated content item 412. Once the transitioning is
complete, the carousel 406 can reference just the curated content
item 412 for selection, as illustrated in the example of FIG.
4C.
[0063] FIG. 5 illustrates an example method 500 for providing
personalized content, according to an embodiment of the present
disclosure. It should be appreciated that there can be additional,
fewer, or alternative steps performed in similar or alternative
orders, or in parallel, within the scope of the various embodiments
discussed herein unless otherwise stated.
[0064] At block 502, a set of candidate content items is generated
from a plurality of content items that are available in the social
networking system, wherein one or more of the candidate content
items are to be included in a personalized content stream for a
first user. At block 504, a corresponding score for each of the
candidate content items is generated with respect to the first
user. At block 506, a first set of content items is determined from
the set of candidate content items based at least in part on the
respective scores, wherein content items in the first set are
included in the personalized content stream.
[0065] FIG. 6 illustrates an example method 600 for reordering a
personalized content stream, according to an embodiment of the
present disclosure. It should be appreciated that there can be
additional, fewer, or alternative steps performed in similar or
alternative orders, or in parallel, within the scope of the various
embodiments discussed herein unless otherwise stated.
[0066] At block 602, information describing a personalized content
stream of a first user is obtained. The personalized content stream
can include a set of content items to be presented to the first
user according to a first ordering. At block 604, a second ordering
for the set of content items is determined based on one or more
criteria. The second ordering satisfies at least one measure of
consistency. At block 606, the personalized content stream is
modified to correspond to the second ordering.
[0067] It is contemplated that there can be many other uses,
applications, and/or variations associated with the various
embodiments of the present disclosure. For example, in some cases,
user can choose whether or not to opt-in to utilize the disclosed
technology. The disclosed technology can also ensure that various
privacy settings and preferences are maintained and can prevent
private information from being divulged. In another example,
various embodiments of the present disclosure can learn, improve,
and/or be refined over time.
Social Networking System--Example Implementation
[0068] FIG. 7 illustrates a network diagram of an example system
700 that can be utilized in various scenarios, in accordance with
an embodiment of the present disclosure. The system 700 includes
one or more user devices 710, one or more external systems 720, a
social networking system (or service) 730, and a network 750. In an
embodiment, the social networking service, provider, and/or system
discussed in connection with the embodiments described above may be
implemented as the social networking system 730. For purposes of
illustration, the embodiment of the system 700, shown by FIG. 7,
includes a single external system 720 and a single user device 710.
However, in other embodiments, the system 700 may include more user
devices 710 and/or more external systems 720. In certain
embodiments, the social networking system 730 is operated by a
social network provider, whereas the external systems 720 are
separate from the social networking system 730 in that they may be
operated by different entities. In various embodiments, however,
the social networking system 730 and the external systems 720
operate in conjunction to provide social networking services to
users (or members) of the social networking system 730. In this
sense, the social networking system 730 provides a platform or
backbone, which other systems, such as external systems 720, may
use to provide social networking services and functionalities to
users across the Internet.
[0069] The user device 710 comprises one or more computing devices
(or systems) that can receive input from a user and transmit and
receive data via the network 750. In one embodiment, the user
device 710 is a conventional computer system executing, for
example, a Microsoft Windows compatible operating system (OS),
Apple OS X, and/or a Linux distribution. In another embodiment, the
user device 710 can be a computing device or a device having
computer functionality, such as a smart-phone, a tablet, a personal
digital assistant (PDA), a mobile telephone, a laptop computer, a
wearable device (e.g., a pair of glasses, a watch, a bracelet,
etc.), a camera, an appliance, etc. The user device 710 is
configured to communicate via the network 750. The user device 710
can execute an application, for example, a browser application that
allows a user of the user device 710 to interact with the social
networking system 730. In another embodiment, the user device 710
interacts with the social networking system 730 through an
application programming interface (API) provided by the native
operating system of the user device 710, such as iOS and ANDROID.
The user device 710 is configured to communicate with the external
system 720 and the social networking system 730 via the network
750, which may comprise any combination of local area and/or wide
area networks, using wired and/or wireless communication
systems.
[0070] In one embodiment, the network 750 uses standard
communications technologies and protocols. Thus, the network 750
can include links using technologies such as Ethernet, 802.11,
worldwide interoperability for microwave access (WiMAX), 3G, 4G,
CDMA, GSM, LTE, digital subscriber line (DSL), etc. Similarly, the
networking protocols used on the network 750 can include
multiprotocol label switching (MPLS), transmission control
protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP),
hypertext transport protocol (HTTP), simple mail transfer protocol
(SMTP), file transfer protocol (FTP), and the like. The data
exchanged over the network 750 can be represented using
technologies and/or formats including hypertext markup language
(HTML) and extensible markup language (XML). In addition, all or
some links can be encrypted using conventional encryption
technologies such as secure sockets layer (SSL), transport layer
security (TLS), and Internet Protocol security (IPsec).
[0071] In one embodiment, the user device 710 may display content
from the external system 720 and/or from the social networking
system 730 by processing a markup language document 714 received
from the external system 720 and from the social networking system
730 using a browser application 712. The markup language document
714 identifies content and one or more instructions describing
formatting or presentation of the content. By executing the
instructions included in the markup language document 714, the
browser application 712 displays the identified content using the
format or presentation described by the markup language document
714. For example, the markup language document 714 includes
instructions for generating and displaying a web page having
multiple frames that include text and/or image data retrieved from
the external system 720 and the social networking system 730. In
various embodiments, the markup language document 714 comprises a
data file including extensible markup language (XML) data,
extensible hypertext markup language (XHTML) data, or other markup
language data. Additionally, the markup language document 714 may
include JavaScript Object Notation (JSON) data, JSON with padding
(JSONP), and JavaScript data to facilitate data-interchange between
the external system 720 and the user device 710. The browser
application 712 on the user device 710 may use a JavaScript
compiler to decode the markup language document 714.
[0072] The markup language document 714 may also include, or link
to, applications or application frameworks such as FLASH.TM. or
Unity.TM. applications, the Silverlight.TM. application framework,
etc.
[0073] In one embodiment, the user device 710 also includes one or
more cookies 716 including data indicating whether a user of the
user device 710 is logged into the social networking system 730,
which may enable modification of the data communicated from the
social networking system 730 to the user device 710.
[0074] The external system 720 includes one or more web servers
that include one or more web pages 722a, 722b, which are
communicated to the user device 710 using the network 750. The
external system 720 is separate from the social networking system
730. For example, the external system 720 is associated with a
first domain, while the social networking system 730 is associated
with a separate social networking domain. Web pages 722a, 722b,
included in the external system 720, comprise markup language
documents 714 identifying content and including instructions
specifying formatting or presentation of the identified content. As
discussed previously, it should be appreciated that there can be
many variations or other possibilities.
[0075] The social networking system 730 includes one or more
computing devices for a social network, including a plurality of
users, and providing users of the social network with the ability
to communicate and interact with other users of the social network.
In some instances, the social network can be represented by a
graph, i.e., a data structure including edges and nodes. Other data
structures can also be used to represent the social network,
including but not limited to databases, objects, classes, meta
elements, files, or any other data structure. The social networking
system 730 may be administered, managed, or controlled by an
operator. The operator of the social networking system 730 may be a
human being, an automated application, or a series of applications
for managing content, regulating policies, and collecting usage
metrics within the social networking system 730. Any type of
operator may be used.
[0076] Users may join the social networking system 730 and then add
connections to any number of other users of the social networking
system 730 to whom they desire to be connected. As used herein, the
term "friend" refers to any other user of the social networking
system 730 to whom a user has formed a connection, association, or
relationship via the social networking system 730. For example, in
an embodiment, if users in the social networking system 730 are
represented as nodes in the social graph, the term "friend" can
refer to an edge formed between and directly connecting two user
nodes.
[0077] Connections may be added explicitly by a user or may be
automatically created by the social networking system 730 based on
common characteristics of the users (e.g., users who are alumni of
the same educational institution). For example, a first user
specifically selects a particular other user to be a friend.
Connections in the social networking system 730 are usually in both
directions, but need not be, so the terms "user" and "friend"
depend on the frame of reference. Connections between users of the
social networking system 730 are usually bilateral ("two-way"), or
"mutual," but connections may also be unilateral, or "one-way." For
example, if Bob and Joe are both users of the social networking
system 730 and connected to each other, Bob and Joe are each
other's connections. If, on the other hand, Bob wishes to connect
to Joe to view data communicated to the social networking system
730 by Joe, but Joe does not wish to form a mutual connection, a
unilateral connection may be established. The connection between
users may be a direct connection; however, some embodiments of the
social networking system 730 allow the connection to be indirect
via one or more levels of connections or degrees of separation.
[0078] In addition to establishing and maintaining connections
between users and allowing interactions between users, the social
networking system 730 provides users with the ability to take
actions on various types of items supported by the social
networking system 730. These items may include groups or networks
(i.e., social networks of people, entities, and concepts) to which
users of the social networking system 730 may belong, events or
calendar entries in which a user might be interested,
computer-based applications that a user may use via the social
networking system 730, transactions that allow users to buy or sell
items via services provided by or through the social networking
system 730, and interactions with advertisements that a user may
perform on or off the social networking system 730. These are just
a few examples of the items upon which a user may act on the social
networking system 730, and many others are possible. A user may
interact with anything that is capable of being represented in the
social networking system 730 or in the external system 720,
separate from the social networking system 730, or coupled to the
social networking system 730 via the network 750.
[0079] The social networking system 730 is also capable of linking
a variety of entities. For example, the social networking system
730 enables users to interact with each other as well as external
systems 720 or other entities through an API, a web service, or
other communication channels. The social networking system 730
generates and maintains the "social graph" comprising a plurality
of nodes interconnected by a plurality of edges. Each node in the
social graph may represent an entity that can act on another node
and/or that can be acted on by another node. The social graph may
include various types of nodes. Examples of types of nodes include
users, non-person entities, content items, web pages, groups,
activities, messages, concepts, and any other things that can be
represented by an object in the social networking system 730. An
edge between two nodes in the social graph may represent a
particular kind of connection, or association, between the two
nodes, which may result from node relationships or from an action
that was performed by one of the nodes on the other node. In some
cases, the edges between nodes can be weighted. The weight of an
edge can represent an attribute associated with the edge, such as a
strength of the connection or association between nodes. Different
types of edges can be provided with different weights. For example,
an edge created when one user "likes" another user may be given one
weight, while an edge created when a user befriends another user
may be given a different weight.
[0080] As an example, when a first user identifies a second user as
a friend, an edge in the social graph is generated connecting a
node representing the first user and a second node representing the
second user. As various nodes relate or interact with each other,
the social networking system 730 modifies edges connecting the
various nodes to reflect the relationships and interactions.
[0081] The social networking system 730 also includes
user-generated content, which enhances a user's interactions with
the social networking system 730. User-generated content may
include anything a user can add, upload, send, or "post" to the
social networking system 730. For example, a user communicates
posts to the social networking system 730 from a user device 710.
Posts may include data such as status updates or other textual
data, location information, images such as photos, videos, links,
music or other similar data and/or media. Content may also be added
to the social networking system 730 by a third party. Content
"items" are represented as objects in the social networking system
730. In this way, users of the social networking system 730 are
encouraged to communicate with each other by posting text and
content items of various types of media through various
communication channels. Such communication increases the
interaction of users with each other and increases the frequency
with which users interact with the social networking system
730.
[0082] The social networking system 730 includes a web server 732,
an API request server 734, a user profile store 736, a connection
store 738, an action logger 740, an activity log 742, and an
authorization server 744. In an embodiment of the invention, the
social networking system 730 may include additional, fewer, or
different components for various applications. Other components,
such as network interfaces, security mechanisms, load balancers,
failover servers, management and network operations consoles, and
the like are not shown so as to not obscure the details of the
system.
[0083] The user profile store 736 maintains information about user
accounts, including biographic, demographic, and other types of
descriptive information, such as work experience, educational
history, hobbies or preferences, location, and the like that has
been declared by users or inferred by the social networking system
730. This information is stored in the user profile store 736 such
that each user is uniquely identified. The social networking system
730 also stores data describing one or more connections between
different users in the connection store 738. The connection
information may indicate users who have similar or common work
experience, group memberships, hobbies, or educational history.
Additionally, the social networking system 730 includes
user-defined connections between different users, allowing users to
specify their relationships with other users. For example,
user-defined connections allow users to generate relationships with
other users that parallel the users' real-life relationships, such
as friends, co-workers, partners, and so forth. Users may select
from predefined types of connections, or define their own
connection types as needed. Connections with other nodes in the
social networking system 730, such as non-person entities, buckets,
cluster centers, images, interests, pages, external systems,
concepts, and the like are also stored in the connection store
738.
[0084] The social networking system 730 maintains data about
objects with which a user may interact. To maintain this data, the
user profile store 736 and the connection store 738 store instances
of the corresponding type of objects maintained by the social
networking system 730. Each object type has information fields that
are suitable for storing information appropriate to the type of
object. For example, the user profile store 736 contains data
structures with fields suitable for describing a user's account and
information related to a user's account. When a new object of a
particular type is created, the social networking system 730
initializes a new data structure of the corresponding type, assigns
a unique object identifier to it, and begins to add data to the
object as needed. This might occur, for example, when a user
becomes a user of the social networking system 730, the social
networking system 730 generates a new instance of a user profile in
the user profile store 736, assigns a unique identifier to the user
account, and begins to populate the fields of the user account with
information provided by the user.
[0085] The connection store 738 includes data structures suitable
for describing a user's connections to other users, connections to
external systems 720 or connections to other entities. The
connection store 738 may also associate a connection type with a
user's connections, which may be used in conjunction with the
user's privacy setting to regulate access to information about the
user. In an embodiment of the invention, the user profile store 736
and the connection store 738 may be implemented as a federated
database.
[0086] Data stored in the connection store 738, the user profile
store 736, and the activity log 742 enables the social networking
system 730 to generate the social graph that uses nodes to identify
various objects and edges connecting nodes to identify
relationships between different objects. For example, if a first
user establishes a connection with a second user in the social
networking system 730, user accounts of the first user and the
second user from the user profile store 736 may act as nodes in the
social graph. The connection between the first user and the second
user stored by the connection store 738 is an edge between the
nodes associated with the first user and the second user.
Continuing this example, the second user may then send the first
user a message within the social networking system 730. The action
of sending the message, which may be stored, is another edge
between the two nodes in the social graph representing the first
user and the second user. Additionally, the message itself may be
identified and included in the social graph as another node
connected to the nodes representing the first user and the second
user.
[0087] In another example, a first user may tag a second user in an
image that is maintained by the social networking system 730 (or,
alternatively, in an image maintained by another system outside of
the social networking system 730). The image may itself be
represented as a node in the social networking system 730. This
tagging action may create edges between the first user and the
second user as well as create an edge between each of the users and
the image, which is also a node in the social graph. In yet another
example, if a user confirms attending an event, the user and the
event are nodes obtained from the user profile store 736, where the
attendance of the event is an edge between the nodes that may be
retrieved from the activity log 742. By generating and maintaining
the social graph, the social networking system 730 includes data
describing many different types of objects and the interactions and
connections among those objects, providing a rich source of
socially relevant information.
[0088] The web server 732 links the social networking system 730 to
one or more user devices 710 and/or one or more external systems
720 via the network 750. The web server 732 serves web pages, as
well as other web-related content, such as Java, JavaScript, Flash,
XML, and so forth. The web server 732 may include a mail server or
other messaging functionality for receiving and routing messages
between the social networking system 730 and one or more user
devices 710. The messages can be instant messages, queued messages
(e.g., email), text and SMS messages, or any other suitable
messaging format.
[0089] The API request server 734 allows one or more external
systems 720 and user devices 710 to call access information from
the social networking system 730 by calling one or more API
functions. The API request server 734 may also allow external
systems 720 to send information to the social networking system 730
by calling APIs. The external system 720, in one embodiment, sends
an API request to the social networking system 730 via the network
750, and the API request server 734 receives the API request. The
API request server 734 processes the request by calling an API
associated with the API request to generate an appropriate
response, which the API request server 734 communicates to the
external system 720 via the network 750. For example, responsive to
an API request, the API request server 734 collects data associated
with a user, such as the user's connections that have logged into
the external system 720, and communicates the collected data to the
external system 720. In another embodiment, the user device 710
communicates with the social networking system 730 via APIs in the
same manner as external systems 720.
[0090] The action logger 740 is capable of receiving communications
from the web server 732 about user actions on and/or off the social
networking system 730. The action logger 740 populates the activity
log 742 with information about user actions, enabling the social
networking system 730 to discover various actions taken by its
users within the social networking system 730 and outside of the
social networking system 730. Any action that a particular user
takes with respect to another node on the social networking system
730 may be associated with each user's account, through information
maintained in the activity log 742 or in a similar database or
other data repository. Examples of actions taken by a user within
the social networking system 730 that are identified and stored may
include, for example, adding a connection to another user, sending
a message to another user, reading a message from another user,
viewing content associated with another user, attending an event
posted by another user, posting an image, attempting to post an
image, or other actions interacting with another user or another
object. When a user takes an action within the social networking
system 730, the action is recorded in the activity log 742. In one
embodiment, the social networking system 730 maintains the activity
log 742 as a database of entries. When an action is taken within
the social networking system 730, an entry for the action is added
to the activity log 742. The activity log 742 may be referred to as
an action log.
[0091] Additionally, user actions may be associated with concepts
and actions that occur within an entity outside of the social
networking system 730, such as an external system 720 that is
separate from the social networking system 730. For example, the
action logger 740 may receive data describing a user's interaction
with an external system 720 from the web server 732. In this
example, the external system 720 reports a user's interaction
according to structured actions and objects in the social
graph.
[0092] Other examples of actions where a user interacts with an
external system 720 include a user expressing an interest in an
external system 720 or another entity, a user posting a comment to
the social networking system 730 that discusses an external system
720 or a web page 722a within the external system 720, a user
posting to the social networking system 730 a Uniform Resource
Locator (URL) or other identifier associated with an external
system 720, a user attending an event associated with an external
system 720, or any other action by a user that is related to an
external system 720. Thus, the activity log 742 may include actions
describing interactions between a user of the social networking
system 730 and an external system 720 that is separate from the
social networking system 730.
[0093] The authorization server 744 enforces one or more privacy
settings of the users of the social networking system 730. A
privacy setting of a user determines how particular information
associated with a user can be shared. The privacy setting comprises
the specification of particular information associated with a user
and the specification of the entity or entities with whom the
information can be shared. Examples of entities with which
information can be shared may include other users, applications,
external systems 720, or any entity that can potentially access the
information. The information that can be shared by a user comprises
user account information, such as profile photos, phone numbers
associated with the user, user's connections, actions taken by the
user such as adding a connection, changing user profile
information, and the like.
[0094] The privacy setting specification may be provided at
different levels of granularity. For example, the privacy setting
may identify specific information to be shared with other users;
the privacy setting identifies a work phone number or a specific
set of related information, such as, personal information including
profile photo, home phone number, and status. Alternatively, the
privacy setting may apply to all the information associated with
the user. The specification of the set of entities that can access
particular information can also be specified at various levels of
granularity. Various sets of entities with which information can be
shared may include, for example, all friends of the user, all
friends of friends, all applications, or all external systems 720.
One embodiment allows the specification of the set of entities to
comprise an enumeration of entities. For example, the user may
provide a list of external systems 720 that are allowed to access
certain information. Another embodiment allows the specification to
comprise a set of entities along with exceptions that are not
allowed to access the information. For example, a user may allow
all external systems 720 to access the user's work information, but
specify a list of external systems 720 that are not allowed to
access the work information. Certain embodiments call the list of
exceptions that are not allowed to access certain information a
"block list". External systems 720 belonging to a block list
specified by a user are blocked from accessing the information
specified in the privacy setting. Various combinations of
granularity of specification of information, and granularity of
specification of entities, with which information is shared are
possible. For example, all personal information may be shared with
friends whereas all work information may be shared with friends of
friends.
[0095] The authorization server 744 contains logic to determine if
certain information associated with a user can be accessed by a
user's friends, external systems 720, and/or other applications and
entities. The external system 720 may need authorization from the
authorization server 744 to access the user's more private and
sensitive information, such as the user's work phone number. Based
on the user's privacy settings, the authorization server 744
determines if another user, the external system 720, an
application, or another entity is allowed to access information
associated with the user, including information about actions taken
by the user.
[0096] In some embodiments, the social networking system 730 can
include a content provider module 746. The content provider module
746 can, for example, be implemented as the content provider module
102 of FIG. 1. In some embodiments, the content provider module
746, in whole or in part, may be implemented in a user device 710
or the external system 720. As discussed previously, it should be
appreciated that there can be many variations or other
possibilities.
Hardware Implementation
[0097] The foregoing processes and features can be implemented by a
wide variety of machine and computer system architectures and in a
wide variety of network and computing environments. FIG. 8
illustrates an example of a computer system 800 that may be used to
implement one or more of the embodiments described herein in
accordance with an embodiment of the invention. The computer system
800 includes sets of instructions for causing the computer system
800 to perform the processes and features discussed herein. The
computer system 800 may be connected (e.g., networked) to other
machines. In a networked deployment, the computer system 800 may
operate in the capacity of a server machine or a client machine in
a client-server network environment, or as a peer machine in a
peer-to-peer (or distributed) network environment. In an embodiment
of the invention, the computer system 800 may be the social
networking system 730, the user device 710, and the external system
820, or a component thereof. In an embodiment of the invention, the
computer system 800 may be one server among many that constitutes
all or part of the social networking system 730.
[0098] The computer system 800 includes a processor 802, a cache
804, and one or more executable modules and drivers, stored on a
computer-readable medium, directed to the processes and features
described herein. Additionally, the computer system 800 includes a
high performance input/output (I/O) bus 806 and a standard I/O bus
808. A host bridge 810 couples processor 802 to high performance
I/O bus 806, whereas I/O bus bridge 812 couples the two buses 806
and 808 to each other. A system memory 814 and one or more network
interfaces 816 couple to high performance I/O bus 806. The computer
system 800 may further include video memory and a display device
coupled to the video memory (not shown). Mass storage 818 and I/O
ports 820 couple to the standard I/O bus 808. The computer system
800 may optionally include a keyboard and pointing device, a
display device, or other input/output devices (not shown) coupled
to the standard I/O bus 808. Collectively, these elements are
intended to represent a broad category of computer hardware
systems, including but not limited to computer systems based on the
x86-compatible processors manufactured by Intel Corporation of
Santa Clara, Calif., and the x86-compatible processors manufactured
by Advanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as
well as any other suitable processor.
[0099] An operating system manages and controls the operation of
the computer system 800, including the input and output of data to
and from software applications (not shown). The operating system
provides an interface between the software applications being
executed on the system and the hardware components of the system.
Any suitable operating system may be used, such as the LINUX
Operating System, the Apple Macintosh Operating System, available
from Apple Computer Inc. of Cupertino, Calif., UNIX operating
systems, Microsoft.RTM. Windows.RTM. operating systems, BSD
operating systems, and the like. Other implementations are
possible.
[0100] The elements of the computer system 800 are described in
greater detail below. In particular, the network interface 816
provides communication between the computer system 800 and any of a
wide range of networks, such as an Ethernet (e.g., IEEE 802.3)
network, a backplane, etc. The mass storage 818 provides permanent
storage for the data and programming instructions to perform the
above-described processes and features implemented by the
respective computing systems identified above, whereas the system
memory 814 (e.g., DRAM) provides temporary storage for the data and
programming instructions when executed by the processor 802. The
I/O ports 820 may be one or more serial and/or parallel
communication ports that provide communication between additional
peripheral devices, which may be coupled to the computer system
800.
[0101] The computer system 800 may include a variety of system
architectures, and various components of the computer system 800
may be rearranged. For example, the cache 804 may be on-chip with
processor 802. Alternatively, the cache 804 and the processor 802
may be packed together as a "processor module", with processor 802
being referred to as the "processor core". Furthermore, certain
embodiments of the invention may neither require nor include all of
the above components. For example, peripheral devices coupled to
the standard I/O bus 808 may couple to the high performance I/O bus
806. In addition, in some embodiments, only a single bus may exist,
with the components of the computer system 800 being coupled to the
single bus. Moreover, the computer system 800 may include
additional components, such as additional processors, storage
devices, or memories.
[0102] In general, the processes and features described herein may
be implemented as part of an operating system or a specific
application, component, program, object, module, or series of
instructions referred to as "programs". For example, one or more
programs may be used to execute specific processes described
herein. The programs typically comprise one or more instructions in
various memory and storage devices in the computer system 800 that,
when read and executed by one or more processors, cause the
computer system 800 to perform operations to execute the processes
and features described herein. The processes and features described
herein may be implemented in software, firmware, hardware (e.g., an
application specific integrated circuit), or any combination
thereof.
[0103] In one implementation, the processes and features described
herein are implemented as a series of executable modules run by the
computer system 800, individually or collectively in a distributed
computing environment. The foregoing modules may be realized by
hardware, executable modules stored on a computer-readable medium
(or machine-readable medium), or a combination of both. For
example, the modules may comprise a plurality or series of
instructions to be executed by a processor in a hardware system,
such as the processor 802. Initially, the series of instructions
may be stored on a storage device, such as the mass storage 818.
However, the series of instructions can be stored on any suitable
computer readable storage medium. Furthermore, the series of
instructions need not be stored locally, and could be received from
a remote storage device, such as a server on a network, via the
network interface 816. The instructions are copied from the storage
device, such as the mass storage 818, into the system memory 814
and then accessed and executed by the processor 802. In various
implementations, a module or modules can be executed by a processor
or multiple processors in one or multiple locations, such as
multiple servers in a parallel processing environment.
[0104] Examples of computer-readable media include, but are not
limited to, recordable type media such as volatile and non-volatile
memory devices; solid state memories; floppy and other removable
disks; hard disk drives; magnetic media; optical disks (e.g.,
Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks
(DVDs)); other similar non-transitory (or transitory), tangible (or
non-tangible) storage medium; or any type of medium suitable for
storing, encoding, or carrying a series of instructions for
execution by the computer system 800 to perform any one or more of
the processes and features described herein.
[0105] For purposes of explanation, numerous specific details are
set forth in order to provide a thorough understanding of the
description. It will be apparent, however, to one skilled in the
art that embodiments of the disclosure can be practiced without
these specific details. In some instances, modules, structures,
processes, features, and devices are shown in block diagram form in
order to avoid obscuring the description. In other instances,
functional block diagrams and flow diagrams are shown to represent
data and logic flows. The components of block diagrams and flow
diagrams (e.g., modules, blocks, structures, devices, features,
etc.) may be variously combined, separated, removed, reordered, and
replaced in a manner other than as expressly described and depicted
herein.
[0106] Reference in this specification to "one embodiment", "an
embodiment", "other embodiments", "one series of embodiments",
"some embodiments", "various embodiments", or the like means that a
particular feature, design, structure, or characteristic described
in connection with the embodiment is included in at least one
embodiment of the disclosure. The appearances of, for example, the
phrase "in one embodiment" or "in an embodiment" in various places
in the specification are not necessarily all referring to the same
embodiment, nor are separate or alternative embodiments mutually
exclusive of other embodiments. Moreover, whether or not there is
express reference to an "embodiment" or the like, various features
are described, which may be variously combined and included in some
embodiments, but also variously omitted in other embodiments.
Similarly, various features are described that may be preferences
or requirements for some embodiments, but not other
embodiments.
[0107] The language used herein has been principally selected for
readability and instructional purposes, and it may not have been
selected to delineate or circumscribe the inventive subject matter.
It is therefore intended that the scope of the invention be limited
not by this detailed description, but rather by any claims that
issue on an application based hereon. Accordingly, the disclosure
of the embodiments of the invention is intended to be illustrative,
but not limiting, of the scope of the invention, which is set forth
in the following claims.
* * * * *