U.S. patent application number 16/856867 was filed with the patent office on 2020-08-06 for clustering users of a social networking system based on user interactions with content items associated with a topic.
This patent application is currently assigned to Facebook, Inc.. The applicant listed for this patent is Facebook, Inc.. Invention is credited to Haibin CHENG, Sue Ann HONG, Xingyao YE.
Application Number | 20200250702 16/856867 |
Document ID | / |
Family ID | 1000004782712 |
Filed Date | 2020-08-06 |
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United States Patent
Application |
20200250702 |
Kind Code |
A1 |
CHENG; Haibin ; et
al. |
August 6, 2020 |
CLUSTERING USERS OF A SOCIAL NETWORKING SYSTEM BASED ON USER
INTERACTIONS WITH CONTENT ITEMS ASSOCIATED WITH A TOPIC
Abstract
A social networking system presents users with a content items
and ad requests, which may include targeting criteria specifying a
topic. Interactions by users who were presented with an
advertisement from an ad request including targeting criteria
specifying the topic are stored by the social networking system and
used to identify a cluster group of additional users having
characteristics similar to characteristics of users who were
presented with the advertisement from the ad request including
targeting criteria specifying the topic and who interacted with the
advertisement. The social networking system determines scores for
additional users in the cluster group based on measures of
similarity between the additional users and the users who were
presented with the advertisement and who interacted with the
advertisement. Based on the determined scores, the social
networking system associates additional users in the cluster group
with the topic.
Inventors: |
CHENG; Haibin; (San Jose,
CA) ; HONG; Sue Ann; (San Francisco, CA) ; YE;
Xingyao; (Mountain View, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Facebook, Inc. |
Menlo Park |
CA |
US |
|
|
Assignee: |
Facebook, Inc.
Menlo Park
CA
|
Family ID: |
1000004782712 |
Appl. No.: |
16/856867 |
Filed: |
April 23, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14719780 |
May 22, 2015 |
10672029 |
|
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16856867 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 12/1859 20130101;
G06Q 30/0255 20130101; H04L 12/18 20130101; G06Q 30/0251 20130101;
G06Q 30/0254 20130101; G06Q 30/0277 20130101; H04L 12/185 20130101;
G06Q 50/01 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/00 20060101 G06Q050/00; H04L 12/18 20060101
H04L012/18 |
Claims
1-20. (canceled)
21. A system, comprising: a processor; a memory storing
instructions, which when executed by the processor, cause the
processor to: identify a cluster group of target users having a
threshold measure of similarity to prior users who have previously
interacted with prior digital content; determine a likelihood that
each of the target users in the cluster group will interact with
new digital content by applying a machine learning model to the
cluster group of target users based on the threshold measure of
similarity; and identify an opportunity to present new digital
content to at least one of the target users based at least in part
of the determined likelihood that the at least one of the target
users will interact with the new digital content, the new digital
content having similar targeting criteria as the prior digital
content.
22. The system of claim 21, wherein the cluster group is identified
based on: identifying at least one characteristic in the prior
digital content with which prior users have previously interacted.
identifying a pool of users that share the at least one
characteristic with the prior users; selecting, from the pool of
users, additional target users to add to the cluster group, based
on a threshold value, wherein the threshold value comprises at
least one of a number characteristics or a percentage of
characteristics shared between the pool of users and the prior
users; and forming the cluster group of target users based on at
least the selected additional target users.
23. The system of claim 22, wherein selecting the additional target
users from the pool of users further comprises: ranking the
additional target users in the cluster group based at least in part
the threshold number or the threshold percentage of the at least
one characteristic that matches the pool of users with the prior
users.
24. The system of claim 22, wherein applying the machine learning
model further comprises: training the machine learning model using
information associated with the at least one identified
characteristic, the threshold measure of similarity, or selected
potential target users, wherein the machine learning model
comprises at least one set of model parameters determined based at
least in part on tendencies of the cluster group of target
users.
25. The system of claim 21, wherein determining a likelihood that
each of the target users in the cluster group will interact with
new digital content comprises: calculating a value for each of the
target users in the cluster group using the machine learning model,
wherein the value represents the likelihood that each of the target
users in the cluster group will interact with new digital content,
and the value is calculated based at least in part on the threshold
measure of a similarity between the target users in the cluster
group and the prior users or information associated with prior
users who have previously interacted with prior digital
content.
26. The system of claim 25, wherein the value is calculated based
at least in part on the threshold measure of a similarity between
the target users in the cluster group and the prior users or
information associated with prior users who have previously
interacted with prior digital content.
27. The system of claim 25, wherein the value is calculated based
at least in part on information associated with prior users who
have previously interacted with prior digital content.
28. The system of claim 21, wherein the opportunity to present new
digital content is further based on one or more digital content
criteria, wherein the one or more digital content criteria is
determined by at least one of a digital content provider, a digital
content host, or a user.
29. The system of claim 21, wherein identifying the opportunity to
present new digital content further comprises: ranking the target
users in the cluster group based at least in part on the determined
likelihood that the at least one of the target users will interact
with the new digital content.
30. The system of claim 21, further comprising: presenting the new
digital content to a mobile device associated with at least one of
the target users in the cluster group based on the identified
opportunity.
31. A method, comprising: identifying, by a processor of a
computing system, a cluster group of target users having a
threshold measure of similarity to prior users who have previously
interacted with prior digital content; determining a likelihood
that each of the target users in the cluster group will interact
with new digital content by applying a machine learning model to
the cluster group of target users based on the threshold measure of
similarity; and identifying an opportunity to present new digital
content to at least one of the target users based at least in part
of the determined likelihood that the at least one of the target
users will interact with the new digital content, the new digital
content having similar targeting criteria as the prior digital
content.
32. The method of claim 31, wherein the cluster group is identified
based on: identifying at least one characteristic in the prior
digital content with which prior users have previously interacted.
identifying a pool of users that share the at least one
characteristic with the prior users; selecting, from the pool of
users, additional target users to add to the cluster group, based
on a threshold value, wherein the threshold value comprises at
least one of a number characteristics or a percentage of
characteristics shared between the pool of users and the prior
users; and forming the cluster group of target users based on at
least the selected additional target users.
33. The method of claim 31, wherein determining a likelihood that
each of the target users in the cluster group will interact with
new digital content comprises: calculating a value for each of the
target users in the cluster group using the machine learning model,
wherein the value represents the likelihood that each of the target
users in the cluster group will interact with new digital content,
and the value is calculated based at least in part on the threshold
measure of a similarity between the target users in the cluster
group and the prior users or information associated with prior
users who have previously interacted with prior digital content,
and wherein the value is calculated based at least in part on the
threshold measure of a similarity between the target users in the
cluster group and the prior users or information associated with
prior users who have previously interacted with prior digital
content.
34. The method of claim 31, wherein the opportunity to present new
digital content is further based on one or more digital content
criteria, wherein the one or more digital content criteria is
determined by at least one of a digital content provider, a digital
content host, or a user.
35. The method of claim 31, wherein identifying the opportunity to
present new digital content further comprises: ranking the target
users in the cluster group based at least in part on the determined
likelihood that the at least one of the target users will interact
with the new digital content.
36. A non-transitory computer-readable storage medium having an
executable stored thereon, which when executed instructs a
processor to, comprising: identifying, by a processor of a
computing system, a cluster group of target users having a
threshold measure of similarity to prior users who have previously
interacted with prior digital content; determining a likelihood
that each of the target users in the cluster group will interact
with new digital content by applying a machine learning model to
the cluster group of target users based on the threshold measure of
similarity; and identifying an opportunity to present new digital
content to at least one of the target users based at least in part
of the determined likelihood that the at least one of the target
users will interact with the new digital content, the new digital
content having similar targeting criteria as the prior digital
content.
37. The non-transitory computer-readable storage medium of claim
36, wherein the cluster group is identified based on: identifying
at least one characteristic in the prior digital content with which
prior users have previously interacted. identifying a pool of users
that share the at least one characteristic with the prior users;
selecting, from the pool of users, additional target users to add
to the cluster group, based on a threshold value, wherein the
threshold value comprises at least one of a number characteristics
or a percentage of characteristics shared between the pool of users
and the prior users; and forming the cluster group of target users
based on at least the selected additional target users.
38. The non-transitory computer-readable storage medium of claim
36, wherein determining a likelihood that each of the target users
in the cluster group will interact with new digital content
comprises: calculating a value for each of the target users in the
cluster group using the machine learning model, wherein the value
represents the likelihood that each of the target users in the
cluster group will interact with new digital content, and the value
is calculated based at least in part on the threshold measure of a
similarity between the target users in the cluster group and the
prior users or information associated with prior users who have
previously interacted with prior digital content, and wherein the
value is calculated based at least in part on the threshold measure
of a similarity between the target users in the cluster group and
the prior users or information associated with prior users who have
previously interacted with prior digital content.
39. The non-transitory computer-readable storage medium of claim
36, wherein the opportunity to present new digital content is
further based on one or more digital content criteria, wherein the
one or more digital content criteria is determined by at least one
of a digital content provider, a digital content host, or a
user.
40. The non-transitory computer-readable storage medium of claim
36, wherein identifying the opportunity to present new digital
content further comprises: ranking the target users in the cluster
group based at least in part on the determined likelihood that the
at least one of the target users will interact with the new digital
content.
Description
BACKGROUND
[0001] This disclosure relates generally to selecting content for
presentation to online system users and more particularly to
targeting content for presentation to groups of users of an online
system.
[0002] Traditionally, providers of content have attempted to tailor
content presented to users based on expected demographics of users.
Even before the advent of broadcast media, a business providing a
product sought to purchase space for content describing or
promoting that product in a publication read by typical consumers
of the product. As publishing and broadcasting costs fell, more
media catered to niche audiences, allowing content providers to
more finely tune presentation of content to narrower groups of
media consumers. Nonetheless, content presented in various media
mainly cater to the typical consumer of the media in which the
content is presented, causing atypical consumers of media to
encounter content that is irrelevant to the atypical consumers.
With the advent of personalized digital media, content may be
matched to an individual user according to known traits of the
user. However, producers of personalized digital media often have
limited information about a user, so a content provider may miss
opportunities to present a user with content relevant to the user
because the content provider lacks explicit user information
indicating that the user has characteristics placing the user in an
audience of users to receive content from the content provider.
SUMMARY
[0003] A social networking system presents users with a variety of
advertisements and content items. An advertisement is included in
an advertisement request ("ad request") including a bid amount
specifying a maximum amount of compensation an advertiser
associated with the ad request will provide the social networking
system for presenting the advertisement. Additionally, the ad
request may include targeting criteria associated with the
advertisement and identifying characteristics of users eligible to
be presented with the advertisement. For example, targeting
criteria user profile information, connections to other users, or
actions, and users having characteristics satisfying at least a
threshold number of the targeting criteria in an ad request are
eligible to be presented with the advertisement in the ad request.
Including targeting criteria in an ad request allows an advertiser
or other content provider to identify users having specific
characteristics, simplifying subsequent distribution of content to
different users. Targeting criteria included in an ad request may
include a topic that identifies a subject or theme of content.
Examples of topics include everyday items such as "coffee," or
"chocolate ice cream," activities such as "skating," or "hunting,"
or any other subject about which content may be presented.
[0004] To select content for presentation to a user, the social
networking system identifies users of the social networking system
who have interacted with advertisements from one or more ad
requests associated with a topic. The social networking system may
identify various ad requests including targeting criteria
specifying the topic and identifies the advertisements associated
included in the identified ad requests. After identifying the ad
requests, the social networking system retrieves stored
interactions with advertisements from the identified ad requests
and identifies users who performed the retrieved interactions. In
some embodiments, the social networking system retrieves actions by
users with the advertisements from the identified ad requests
including the topic in targeting criteria that occurred within a
specified time interval (e.g., within a threshold time interval
from a current time, within a specific time interval).
[0005] Based on characteristics of the identified users, the social
networking system identifies a cluster group that includes
additional users having characteristics matching or similar to
characteristics of the identified users. In various embodiments,
the social networking system trains a model based on
characteristics of the identified users and applies the trained
model to other users of the social networking system. Based on
application of the model, the social networking system identifies
additional users for inclusion in the cluster group. For example,
application of the model to characteristics of a user generates a
value based on similarity between characteristics of the user and
characteristics of the identified users; if the value for the user
equals or exceeds a threshold value, the social networking system
includes the user in the cluster group.
[0006] Additionally, the social networking system determines a
score for users in the cluster group. A score associated a user of
the cluster group provides a measure of similarity between the user
of the cluster group and the identified users. In some embodiments,
the measure of similarity used to determine the score represents an
affinity of a user in the cluster group for content associated with
the cluster group, a likelihood of a user in the cluster group
interacting with advertisements with which the identified users
previously interacted, or a similarity in tastes, preferences,
actions, or interests between a user in the cluster group and the
identified users. The social networking system may train and apply
a scoring model to characteristics of users in the cluster group to
determine the scores associated with the users in the cluster
group. In some embodiments, the scoring model also accounts for
characteristics of the identified users.
[0007] Based at least in part on the scores associated with users
in the cluster group, the social networking system selects users
from the cluster group. In one example, the social networking
system ranks users in the cluster group based on their associated
scores and selects users in the cluster group having at least a
threshold position in the ranking. As another example, the social
networking system selects users in the cluster group associated
with scores equaling or exceeding a threshold value. In another
embodiment, the social networking system identifies a number of
topics associated with each user, and selects users in the cluster
group based on the number of topics associated with each user and
the determined score associated with each user. For example, the
social networking system selects users from the cluster group
having at least a threshold score and also associated with less
than a threshold number of topics. The social networking system
identifies the selected users from the cluster group as associated
with the topic associated with the ad request including the
advertisement with which the identified users interacted.
Identifying the selected users as associated with the topic
associated with the ad request including the advertisement with
which the identified users interacted allows the social networking
system to identify additional content associated with the topic as
eligible for presentation to the selected users, increasing the
amount of content available for presentation to the selected users
with which the selected users are likely to interact.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a block diagram of a system environment in which a
social networking system operates, in accordance with an
embodiment.
[0009] FIG. 2 is a block diagram of a social networking system, in
accordance with an embodiment.
[0010] FIG. 3 is a flow chart of a method for identifying a cluster
group of users of the social networking system, in accordance with
an embodiment.
[0011] FIG. 4 is a diagram illustrating a cluster group of the
users of the social networking system, in accordance with an
embodiment.
[0012] The figures depict various embodiments for purposes of
illustration only. One skilled in the art will readily recognize
from the following discussion that alternative embodiments of the
structures and methods illustrated herein may be employed without
departing from the principles described herein.
DETAILED DESCRIPTION
System Architecture
[0013] FIG. 1 is a block diagram of a system environment 100 for a
social networking system 140. The system environment 100 shown by
FIG. 1 comprises one or more client devices 110, a network 120, one
or more third-party systems 130, and the social networking system
140. In alternative configurations, different and/or additional
components may be included in the system environment 100. The
embodiments described herein can be adapted to online systems other
than social networking systems.
[0014] The client devices 110 are one or more computing devices
capable of receiving user input as well as transmitting and/or
receiving data via the network 120. In one embodiment, a client
device 110 is a conventional computer system, such as a desktop or
a laptop computer. Alternatively, a client device 110 may be a
device having computer functionality, such as a personal digital
assistant (PDA), a mobile telephone, a smartphone or another
suitable device. A client device 110 is configured to communicate
via the network 120. In one embodiment, a client device 110
executes an application allowing a user of the client device 110 to
interact with the social networking system 140. For example, a
client device 110 executes a browser application to enable
interaction between the client device 110 and the social networking
system 140 via the network 120. In another embodiment, a client
device 110 interacts with the social networking system 140 through
an application programming interface (API) running on a native
operating system of the client device 110, such as IOS.RTM. or
ANDROID.TM..
[0015] The client devices 110 are configured to communicate via the
network 120, which may comprise any combination of local area
and/or wide area networks, using both wired and/or wireless
communication systems. In one embodiment, the network 120 uses
standard communications technologies and/or protocols. For example,
the network 120 includes communication links using technologies
such as Ethernet, 802.11, worldwide interoperability for microwave
access (WiMAX), 3G, 4G, code division multiple access (CDMA),
digital subscriber line (DSL), etc. Examples of networking
protocols used for communicating via the network 120 include
multiprotocol label switching (MPLS), transmission control
protocol/Internet protocol (TCP/IP), hypertext transport protocol
(HTTP), simple mail transfer protocol (SMTP), and file transfer
protocol (FTP). Data exchanged over the network 120 may be
represented using any suitable format, such as hypertext markup
language (HTML) or extensible markup language (XML). In some
embodiments, all or some of the communication links of the network
120 may be encrypted using any suitable technique or
techniques.
[0016] One or more third party systems 130 may be coupled to the
network 120 for communicating with the social networking system
140, which is further described below in conjunction with FIG. 2.
In one embodiment, a third party system 130 is an application
provider communicating information describing applications for
execution by a client device 110 or communicating data to client
devices 110 for use by an application executing on the client
device. In other embodiments, a third party system 130 provides
content or other information for presentation via a client device
110. A third party system 130 may also communicate information to
the social networking system 140, such as advertisements, content,
information describing a group of users of the social networking
system 140, or information about an application provided by the
third party system 130. In some embodiments, a third party system
130 may communicate information directly to the social networking
system 140.
[0017] FIG. 2 is a block diagram of an architecture of the social
networking system 140. The social networking system 140 shown in
FIG. 2 includes a user profile store 205, a content store 210, an
action logger 215, an action log 220, an edge store 225, an
advertisement request ("ad request") store 230, a cluster group
generation module 235, a content selection module 240 and a web
server 245. In other embodiments, the social networking system 140
may include additional, fewer, or different components for various
applications. Conventional components such as network interfaces,
security functions, 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 architecture.
[0018] Each user of the social networking system 140 is associated
with a user profile, which is stored in the user profile store 205.
A user profile includes declarative information about the user that
was explicitly shared by the user and may also include profile
information inferred by the social networking system 140. In one
embodiment, a user profile includes multiple data fields, each
describing one or more attributes of the corresponding social
networking system user. Examples of information stored in a user
profile include biographic, demographic, and other types of
descriptive information, such as work experience, educational
history, gender, hobbies or preferences, location and the like. A
user profile may also store other information provided by the user,
for example, images or videos. In certain embodiments, images of
users may be tagged with information identifying the social
networking system users displayed in an image, with information
identifying the images in which a user is tagged stored in the user
profile of the user. A user profile in the user profile store 205
may also maintain references to actions by the corresponding user
performed on content items in the content store 210 and stored in
the action log 220.
[0019] While user profiles in the user profile store 205 are
frequently associated with individuals, allowing individuals to
interact with each other via the social networking system 140, user
profiles may also be stored for entities such as businesses or
organizations. This allows an entity to establish a presence on the
social networking system 140 for connecting and exchanging content
with other social networking system users. The entity may post
information about itself, about its products or provide other
information to users of the social networking system 140 using a
brand page associated with the entity's user profile. Other users
of the social networking system 140 may connect to the brand page
to receive information posted to the brand page or to receive
information from the brand page. A user profile associated with the
brand page may include information about the entity itself,
providing users with background or informational data about the
entity.
[0020] The content store 210 stores objects that each represent
various types of content. Examples of content represented by an
object include a page post, a status update, a photograph, a video,
a link, a shared content item, a gaming application achievement, a
check-in event at a local business, a brand page, or any other type
of content. Social networking system users may create objects
stored by the content store 210, such as status updates, photos
tagged by users to be associated with other objects in the social
networking system 140, events, groups or applications. In some
embodiments, objects are received from third-party applications or
third-party applications separate from the social networking system
140. In one embodiment, objects in the content store 210 represent
single pieces of content, or content "items." Hence, social
networking system users are encouraged to communicate with each
other by posting text and content items of various types of media
to the social networking system 140 through various communication
channels. This increases the amount of interaction of users with
each other and increases the frequency with which users interact
within the social networking system 140.
[0021] The action logger 215 receives communications about user
actions internal to and/or external to the social networking system
140, populating the action log 220 with information about user
actions. Examples of actions include adding a connection to another
user, sending a message to another user, uploading an image,
reading a message from another user, viewing content associated
with another user, and attending an event posted by another user.
In addition, a number of actions may involve an object and one or
more particular users, so these actions are associated with the
particular users as well and stored in the action log 220.
[0022] The action log 220 may be used by the social networking
system 140 to track user actions on the social networking system
140, as well as actions on third party systems 130 that communicate
information to the social networking system 140. Users may interact
with various objects on the social networking system 140, and
information describing these interactions is stored in the action
log 220. Examples of interactions with objects include: commenting
on posts, sharing links, and checking-in to physical locations via
a client device 110, accessing content items, and any other
suitable interactions. Additional examples of interactions with
objects on the social networking system 140 that are included in
the action log 220 include: commenting on a photo album,
communicating with a user, establishing a connection with an
object, joining an event, joining a group, creating an event,
authorizing an application, using an application, expressing a
preference for an object ("liking" the object), and engaging in a
transaction. Additionally, the action log 220 may record a user's
interactions with advertisements on the social networking system
140 as well as with other applications operating on the social
networking system 140. In some embodiments, data from the action
log 220 is used to infer user characteristics of a user (e.g.,
interests or preferences), augmenting the user characteristics
included in the user's user profile and allowing a more complete
understanding of user preferences and characteristics.
[0023] The action log 220 may also store user actions taken on a
third party system 130, such as an external website, and
communicated to the social networking system 140. For example, an
e-commerce website may recognize a user of a social networking
system 140 through a social plug-in enabling the e-commerce website
to identify the user of the social networking system 140. Because
users of the social networking system 140 are uniquely
identifiable, e-commerce websites, such as in the preceding
example, may communicate information about a user's actions outside
of the social networking system 100 to the social networking system
140 for association with the user. Hence, the action log 220 may
record information about actions users perform on a third party
system 220, including webpage viewing histories, advertisements
that were engaged, purchases made, and other patterns from shopping
and buying. Additionally, actions a user performs via an
application associated with a third party system 130 and executing
on a client device 110 may be communicated to the action logger 215
by the application for recordation and association with the user in
the action log 220.
[0024] In one embodiment, the edge store 225 stores information
describing connections between users and other objects on the
social networking system 140 as edges. Some edges may be defined by
users, allowing users to specify their relationships with other
users. For example, users may generate edges with other users that
parallel the users' real-life relationships, such as friends,
co-workers, partners, and so forth. Other edges are generated when
users interact with objects in the social networking system 140,
such as expressing interest in a page on the social networking
system 140, sharing a link with other users of the social
networking system 140, and commenting on posts made by other users
of the social networking system 140.
[0025] In one embodiment, an edge may include various features each
representing characteristics of interactions between users,
interactions between users and objects, or interactions between
objects. For example, features included in an edge describe a rate
of interaction between two users, how recently two users have
interacted with each other, a rate or an amount of information
retrieved by one user about an object, or numbers and types of
comments posted by a user about an object. The features may also
represent information describing a particular object or user. For
example, a feature may represent the level of interest that a user
has in a particular topic, the rate at which the user logs into the
social networking system 140, or information describing demographic
information about the user. Each feature may be associated with a
source object or user, a target object or user, and a feature
value. A feature may be specified as an expression based on values
describing the source object or user, the target object or user, or
interactions between the source object or user and target object or
user; hence, an edge may be represented as one or more feature
expressions.
[0026] The edge store 225 also stores information about edges, such
as affinity scores for objects, interests, and other users.
Affinity scores, or "affinities," may be computed by the social
networking system 140 over time to approximate a user's interest in
an object or in another user in the social networking system 140
based on the actions performed by the user. A user's affinity may
be computed by the social networking system 140 over time to
approximate the user's interest in an object, in a topic, or in
another user in the social networking system 140 based on actions
performed by the user. Computation of affinity is further described
in U.S. patent application Ser. No. 12/978,265, filed on Dec. 23,
2010, U.S. patent application Ser. No. 13/690,254, filed on Nov.
30, 2012, U.S. patent application Ser. No. 13/689,969, filed on
Nov. 30, 2012, and U.S. patent application Ser. No. 13/690,088,
filed on Nov. 30, 2012, each of which is hereby incorporated by
reference in its entirety. Multiple interactions between a user and
a specific object may be stored as a single edge in the edge store
225, in one embodiment. Alternatively, each interaction between a
user and a specific object is stored as a separate edge. In some
embodiments, connections between users may be stored in the user
profile store 205, or the user profile store 205 may access the
edge store 225 to determine connections between users.
[0027] One or more advertisement requests ("ad requests") are
included in the ad request store 230. An advertisement request
includes advertisement content and a bid amount. The advertisement
content is text, image, audio, video, or any other suitable data
presented to a user. In various embodiments, the advertisement
content also includes a landing page specifying a network address
to which a user is directed when the advertisement is accessed. The
bid amount is associated with an ad request by an advertiser and is
used to determine an expected value, such as monetary compensation,
provided by an advertiser to the social networking system 140 if
advertisement content in the ad request is presented to a user, if
the advertisement content in the ad request receives a user
interaction when presented, or if any suitable condition is
satisfied when advertisement content in the ad request is presented
to a user. For example, the bid amount specifies a monetary amount
that the social networking system 140 receives from the advertiser
if advertisement content in an ad request is displayed. In some
embodiments, the expected value to the social networking system 140
of presenting the advertisement content may be determined by
multiplying the bid amount by a probability of the advertisement
content being accessed by a user.
[0028] Additionally, an advertisement request may include one or
more targeting criteria specified by the advertiser. Targeting
criteria included in an advertisement request specify one or more
characteristics of users eligible to be presented with
advertisement content in the advertisement request. For example,
targeting criteria are used to identify users having user profile
information, edges, or actions satisfying at least one of the
targeting criteria. Hence, targeting criteria allow an advertiser
to identify users having specific characteristics, simplifying
subsequent distribution of content to different users.
[0029] In one embodiment, targeting criteria may specify actions or
types of connections between a user and another user or object of
the social networking system 140. Targeting criteria may also
specify interactions between a user and objects performed external
to the social networking system 140, such as on a third party
system 130. For example, targeting criteria identifies users that
have taken a particular action, such as sent a message to another
user, used an application, joined a group, left a group, joined an
event, generated an event description, purchased or reviewed a
product or service using an online marketplace, requested
information from a third party system 130, installed an
application, or performed any other suitable action. Including
actions in targeting criteria allows advertisers to further refine
users eligible to be presented with advertisement content from an
advertisement request. As another example, targeting criteria
identifies users having a connection to another user or object or
having a particular type of connection to another user or
object.
[0030] Targeting criteria may specify a topic, which refers to a
subject or a theme associated with content. Examples of topics
include everyday items such as "coffee," or "chocolate ice cream,"
activities such as "skating," or "hunting," or any other subject
describing content. Including one or more topics in targeting
criteria allows an advertiser to further refine users eligible to
be presented with advertisement content from an advertisement
request or to identify users more likely to interact with
advertisement content from an advertisement request. This allows
advertisers to specifically target content to users who have
previously interacted with content associated with a topic or who
have indicated an interest in the topic, which may increase the
likelihood of the users interacting with the content targeted by
the advertisers.
[0031] The cluster group generation module 235 identifies cluster
groups associated with various topics. A cluster group associated
with a topic includes social networking system users having at
least a threshold affinity for content associated with the topic.
In some embodiments, the cluster group generation module 235
identifies a topic and identifies users who have previously
interacted with content items associated with the topic. The
cluster group generation module 235 may identify users who
interacted with content items associated with an identified topic
within a specific time interval (e.g., within a specific time
interval from a current time, within a specific range of dates
and/or times). Based on the identified users, the cluster group
generation module 235 identifies additional users having at least a
threshold measure of similarity to the identified users who
interacted with content items associated with a topic for inclusion
in the cluster group associated with the topic. As further
described below in conjunction with FIG. 3, the cluster group
generation 235 module determines measures of similarity between
additional users and the identified users based on characteristics
of the additional users and characteristics of the additional
users.
[0032] For additional users in the cluster group, the cluster group
generation module 235 determines a score for users in the cluster
group. A score associated an additional user in the cluster group
also provides a measure of similarity between the additional user
in the cluster group and the identified users. In some embodiments,
the measure of similarity used to determine the score represents an
affinity of an additional user in the cluster group for content
associated with the cluster group, a likelihood of the additional
user in the cluster group interacting with advertisements with
which the identified users previously interacted, or a similarity
in tastes, preferences, actions, or interests between the
additional user in the cluster group and the identified users. The
cluster group generation module 235 may train and apply a scoring
model to characteristics of additional users in the cluster group
to determine the scores associated with the users in the cluster
group. In some embodiments, the scoring model also accounts for
characteristics of the identified users. Generation of scores
associated with additional users in the cluster group is further
described below in conjunction with FIG. 3. Based on the scores
associated with additional users in the cluster group, the cluster
group identification module 235 selects additional users from the
cluster group to associate with the topic, as further described
below in conjunction with FIG. 3. Additional users identified as
associated with the topic are eligible to be presented with
additional content items associated with the topic.
[0033] The content selection module 240 selects one or more content
items for communication to a client device 110 to be presented to a
user. Content items eligible for presentation to the user are
retrieved from the content store 210, from the ad request store
230, or from another source by the content selection module 240,
which selects one or more of the content items for presentation to
the viewing user. A content item eligible for presentation to the
user is a content item associated with at least a threshold number
of targeting criteria satisfied by characteristics of the user or
is a content item that is not associated with targeting criteria.
When identifying content items eligible for presentation to a user,
the content selection module 240 accounts for topics associated
with the user by the cluster group generation module 235 when
comparing characteristics of the user to targeting criteria
included in content items or ad requests. In various embodiments,
the content selection module 240 includes content items eligible
for presentation to the user in one or more selection processes,
which identify a set of content items for presentation to the
viewing user. For example, the content selection module 240
determines measures of relevance of various content items to the
user based on characteristics associated with the user by the
social networking system 140 and based on the user's affinity for
different content items. Based on the measures of relevance, the
content selection module 240 selects content items for presentation
to the user. As an additional example, the content selection module
240 selects content items having the highest measures of relevance
or having at least a threshold measure of relevance for
presentation to the user. Alternatively, the content selection
module 240 ranks content items based on their associated measures
of relevance and selects content items having the highest positions
in the ranking or having at least a threshold position in the
ranking for presentation to the user.
[0034] Content items eligible for presentation to the user may
include ad requests or other content items associated with bid
amounts. The content selection module 240 uses the bid amounts
associated with ad requests when selecting content for presentation
to the viewing user. In various embodiments, the content selection
module 240 determines an expected value associated with various ad
requests (or other content items) based on their bid amounts and
selects content items associated with a maximum expected value or
associated with at least a threshold expected value for
presentation. An expected value associated with an ad request or
with a content item represents an expected amount of compensation
to the social networking system 140 for presenting an ad request or
a content item. For example, the expected value associated with an
ad request is a product of the ad request's bid amount and a
likelihood of the user interacting with the ad content from the ad
request. The content selection module 240 may rank ad requests
based on their associated bid amounts and select ad requests having
at least a threshold position in the ranking for presentation to
the user. In some embodiments, the content selection module 240
ranks both content items not associated with bid amounts and ad
requests in a unified ranking based on bid amounts associated with
ad requests and measures of relevance associated with content items
and ad requests. Based on the unified ranking, the content
selection module 240 selects content for presentation to the user.
Selecting ad requests and other content items through a unified
ranking is further described in U.S. patent application Ser. No.
13/545,266, filed on Jul. 10, 2012, which is hereby incorporated by
reference in its entirety.
[0035] For example, the content selection module 240 receives a
request to present a feed of content to a user of the social
networking system 140. The feed may include one or more
advertisements as well as content items, such as stories describing
actions associated with other online system users connected to the
user. The content selection module 240 accesses one or more of the
user profile store 205, the content store 210, the action log 220,
the edge store 225, and the cluster group generation module 235 to
retrieve information about the user. For example, information
describing actions associated with other users connected to the
user or other data associated with users connected to the user are
retrieved. Additionally, one or more advertisement requests ("ad
requests") may be retrieved from the ad request store 230. The
retrieved stories, ad requests, or other content items, are
analyzed by the content selection module 240 to identify candidate
content items, including ad requests, eligible for presentation to
the user. For example, content items associated with users who not
connected to the user or stories associated with users for whom the
user has less than a threshold affinity are discarded as candidate
content items. Based on various criteria, the content selection
module 240 selects one or more of the content items or ad requests
identified as candidate content for presentation to the identified
user. The selected content items or ad requests are included in a
feed of content that is presented to the user. For example, the
feed of content includes at least a threshold number of content
items describing actions associated with users connected to the
user via the social networking system 140.
[0036] In various embodiments, the content selection module 240
presents content to a user through a feed including a plurality of
content items selected for presentation to the user. One or more
advertisements may also be included in the feed. The content
selection module 240 may also determine the order in which selected
content items or advertisements are presented via the feed. For
example, the content selection module 240 orders content items or
advertisements in the feed based on likelihoods of the user
interacting with various content items or advertisements.
[0037] The web server 245 links the social networking system 140
via the network 120 to the one or more client devices 110, as well
as to the one or more third party systems 130. In some embodiments,
the web server 245 links the social networking system 140 directly
ton one or more third party systems 130. The web server 245 serves
web pages, as well as other content, such as JAVA.RTM., FLASH.RTM.,
XML and so forth. The web server 245 may receive and route messages
between the social networking system 140 and the client device 110,
for example, instant messages, queued messages (e.g., email), text
messages, short message service (SMS) messages, or messages sent
using any other suitable messaging technique. A user may send a
request to the web server 245 to upload information (e.g., images
or videos) that are stored in the content store 210. Additionally,
the web server 250 may provide application programming interface
(API) functionality to send data directly to native client device
operating systems, such as IOS.RTM., ANDROID.TM., WEBOS.RTM. or
BlackberryOS.
Identifying a Cluster Group Associated with a Topic
[0038] FIG. 3 is a flowchart of one embodiment of a method for
identifying a cluster group of users of the social networking
system 140. In other embodiments, the method may include different
and/or additional steps than those described in conjunction with
FIG. 3. Additionally, in some embodiments, the method may perform
the steps described in conjunction with FIG. 3 in different
orders.
[0039] The social networking system 140 presents users with a
variety of advertisements included in ad requests and other content
items. Various ad requests and content items are associated with
topics. A topic associated with a content item or with an ad
request identifies a subject described by the content item or the
ad request. In some embodiments, an ad request or a content item
includes targeting criteria that specifies characteristics of
social networking system users eligible to be presented with the ad
request or the content item. Targeting criteria may identify a
topic, so the social networking system 140 associates a content
item or an ad request with the topic included in the targeting
criteria. Different users may interact with content items or
advertisements from ad requests associated with different topics,
so identifying topics associated with ad requests or content items
with which a user interacted allows the social networking system
140 to select content for presentation to the user with which the
user is more likely to interact.
[0040] Based on interactions with advertisements from ad requests
or with content items presented to users that are stored by the
social networking system 140, the social networking system 140
identifies 310 users who have interacted with advertisements from
one or more ad requests associated with the topic or with content
items associated with a topic. For example, the social networking
system 140 identifies 310 users who were presented with an
advertisement included in an ad request including a specific topic
as targeting criteria and who performed one or more specific
interactions with the advertisement included in the ad request
(e.g., expressed a preference for the advertisement, shared the
advertisement with another user, accessed content associated with
the advertisement, accessed content associated with the
advertisement, etc.). In some embodiments, the social networking
system 140 identifies 310 users who interacted with advertisements
from ad requests or with content items associated with the topic
within a specified time interval. Additionally, the social
networking system 140 may identify 310 users who performed a
specific interaction (e.g., commented on, shared) with
advertisements included in ad requests associated with the topic or
with content items associated with the topic or who performed a
specific interaction with ad requests or content items within a
specified time interval. For example, the social networking system
140 identifies ad requests or content items associated with the
topic and identifies 310 users who interacted with advertisements
included in the identified ad requests or with content items based
on information stored by the social networking system 140
describing interactions associated with the identified ad requests
and users associated with the interactions.
[0041] The social networking system 140 identifies 320 a cluster
group of users associated with the topic based on the
characteristics of the identified users. As described above in
conjunction with FIG. 2, a cluster group associated with the topic
includes additional users having at least a threshold measure of
similarity to one or more users who interacted with a content item
associated with the topic or with an advertisement included in an
ad request associated with the topic. For example, the cluster
group associated with the topic includes additional users having at
least a threshold number or percentage of characteristics matching
or similar to characteristics of users who interacted with an
advertisement included in an ad request associated with the topic
or with a content item associated with the topic. In some
embodiments, the social networking system 140 identifies 320
additional users having at least a threshold number or percentage
of interests matching interests specified by at least a threshold
number of users who interacted with advertisements included in ad
requests associated with the topic or with content items associated
with the topic. Similarly, the social networking system 140 may
identify 320 additional users who interacted with content items or
advertisements from ad requests having at least a threshold number
or percentage of characteristics matching characteristics of
content items with which the users interacted or ad requests
associated with the topic and including advertisements with which
the users interacted. The social networking system 140 may train
and apply a model to characteristics of the users who interacted
with content items associated with the topic or with advertisements
included in ad requests associated with the topic, characteristics
of additional users, characteristics of content items with which
the users interacted or advertisements included in ad requests with
which the users interacted, and/or characteristics of content items
with which the user interacted or characteristics of ad requests
including advertisements with which the additional users interacted
to generate values associated with the additional users. Based on
the values associated with additional users, the social networking
system 140 identifies 320 additional users included in the cluster
group. For example, additional users associated with values
equaling or exceeding a threshold are identified 320 as included in
the cluster group. The value associated with an additional user
provides a measure of similarity between the additional user and
the users who interacted with content items associated with the
topic or with advertisements included in ad requests associated
with the topic. In some embodiments, the model includes a set of
model parameters determined by training a machine learning
algorithm based on characteristics of users who interacted with ad
requests or content items associated with the topic. The cluster
group may include both the users who interacted with ad requests or
content items and the identified additional users in some
embodiments.
[0042] Based on characteristics of the additional users in the
cluster group, the social networking system 140 determines 330
scores for the various additional users in the cluster group. A
score for an additional user in the cluster group is based on a
measure of similarity between the additional user and users who
interacted with an ad request or a content item associated with the
topic associated with the cluster group. In some embodiments, the
score for an additional user is the value associated with the
additional user by the social networking system 140 when
identifying 320 additional users for inclusion in the cluster
group. Alternatively, in other embodiments, the score for an
additional user may provide a measure of the additional user's
affinity for content associated with the topic associated with
cluster group, a likelihood of the additional user interacting with
content items previously interacted with by the users or with
advertisements included in ad requests previously interacted with
by the users, or a measure of similarity of characteristics of the
additional user and characteristics (e.g., interests, actions,
demographic information) of the users who interacted with the
advertisements included in ad requests associated with the topic
associated with the cluster group or interacted with content items
associated with the topic associated with the cluster group. The
social networking system 140 may train and apply a scoring model to
characteristics of an additional user in the cluster group and
characteristics of the users who interacted with advertisements
included in ad requests associated with the topic associated with
the cluster group or with content items associated with the topic
associated with the cluster group to determine a score for the
additional user. In one embodiment, the model includes a set of
model parameters determined by training a machine learning
algorithm based on characteristics of users who interacted with ad
requests or content items associated with the topic. An example of
determining 330 a score associated with users in the cluster group
is further described in U.S. patent application Ser. No.
14/290,355, filed on May 29, 2014 which is hereby incorporated by
reference in its entirety.
[0043] Based at least in part on the scores for the additional
users in the cluster group, the social networking system 140
selects 340 users from the cluster group. In some embodiments, the
social networking system 140 selects 340 at least a threshold
number or percentage of the additional users in the cluster group.
For example, the social networking system 140 ranks the additional
users in the cluster group based on the scores for the additional
users, and selects 340 additional users having at least a threshold
position in the ranking. The threshold position in the ranking may
correspond to the threshold number of additional users or may be
determined based on a number of additional users in the target
group and a threshold percentage of the additional users.
Alternatively, the social networking system 140 selects 340
additional users from the cluster group associated with scores
equaling or exceeding a threshold score; the threshold score may be
determined based on scores associated with various additional users
and a threshold percentage of the additional users so a number of
additional users equaling or exceeding the threshold percentage is
selected 340.
[0044] In another example, the social networking system 140
identifies a number of topics associated with each additional user
in the cluster group and selects 340 additional users from the
cluster group based on scores associated with additional users in
the cluster group and numbers of topics associated with additional
users in the cluster group. For example, the social networking
system 140 selects 340 additional users from the cluster group
having a score equaling or exceeding a threshold score and
associated with less than a threshold number of topics. In some
embodiments, the social networking system 140 may select 340
additional users based on a combination of the scores associated
with additional users and the number of topics associated with
additional users. For example, the social networking system 140
selects 340 an additional user associated with less than a
threshold number of topics if the score associated with the
additional user exceeds a threshold score.
[0045] The social networking system 140 identifies 350 the selected
additional users from the cluster group as being associated with
the topic associated with the cluster group. For example, the
social networking system 140 includes information identifying the
topic in user profiles associated with the selected additional
users or stores information associating user identifiers of the
selected additional users with the topic. Identifying 350 the
selected additional users as being associated with the topic allows
the social networking system 140 to identify the selected
additional users as eligible to be presented with additional
content items or ad requests that include the topic in targeting
criteria. This allows the social networking system 140 to increase
the number of users who may be presented with content items or ad
requests including the topic in targeting criteria to include users
who have not explicitly associated themselves with the topic.
Identifying additional users based on similarity between the
additional users and users who interacted with content items or ad
requests associated with a topic increases the likelihood of the
additional users also interacting with subsequently presented
content items or ad requests including the topic in targeting
criteria.
[0046] After identifying 350 the selected additional users from the
cluster group as associated with the topic, the social networking
system 140 identifies 360 an opportunity to present content items,
including one or more advertisements to a selected additional user.
The social networking system 140 determines 370 the selected
additional user is eligible to be presented with advertisements
included in ad requests that include the topic in targeting
criteria or content items that include the topic in targeting
criteria and have at least a threshold number of targeting criteria
satisfied by characteristics of the selected additional user. For
example, the social networking system 140 receives a request for
content from a client device 110 associated with the selected
additional user and selects candidate content to evaluate for
presentation to the selected additional user that includes content
items having targeting criteria including the topic or
advertisements included in ad requests having targeting criteria
including the topic. Content items and/or ad requests eligible for
presentation to the selected additional user, which may include
content items or ad requests having targeting criteria identifying
the topic, are included in one or more selection processes, as
described above in conjunction with FIG. 2, which select content
for presentation to the selected additional user. For example, the
social networking system 140 includes an advertisement request
having targeting criteria identifying the topic in a selection
process along with other ad requests eligible for presentation to
the selected additional user. Based at least in part on bid amounts
associated with the ad requests, the social networking system 140
selects advertisements included in one or more ad requests for
presentation to the selected additional user.
Cluster Group Including a Subset of Users of the Social Networking
System
[0047] FIG. 4 is a diagram of an example cluster group including
social networking system users. The social networking system 140
includes a plurality of users 400 who are presented with content,
which includes ad requests, by the social networking system 140.
Various users interact with different content items and
advertisements included in ad requests selected for presentation to
the users by the social networking system 140. A subset of the
users 400 who interacted with one or more advertisements included
in ad requests including targeting criteria specifying a specific
topic or with content items including targeting criteria
identifying the specific topic are included in a target group 410.
Hence, the target group 410 includes users associated with the
specific topic and who have interacted with advertisements included
in ad requests including targeting criteria identifying the
specific topic or with content items presented to the users that
include targeting criteria identifying the specific topic. Stored
actions associated with users and characteristics of users
maintained by the social networking system 140 may be used to
identify the target group 410 in various embodiments.
[0048] As described above in conjunction with FIG. 3, the social
networking system 140 identifies a cluster group 420 of users
associated with the specific topic based at least in part on
characteristics of users in the target group 410. In one
embodiment, the cluster group 420 includes a subset of the
plurality of users 400 who have at least a threshold measure of
similarity to users in the target group 410, as described above in
conjunction with FIG. 3. For example, the social networking system
140 applies a trained model to characteristics of users in the
target group 410 and to characteristics of users from the plurality
of users 400 to generate a value and includes users associated with
at least a threshold value in the cluster group 420.
[0049] As described above in conjunction with FIG. 3, the social
networking system 140 determines scores for users in the cluster
group 420 based at least in part on measures of similarity between
users in the cluster group 420 and users in the target group 410.
Based on the scores, the social networking system 140 identifies
users from the cluster group 420 as associated with the specific
topic associated with ad requests including advertisements with
which users in the target group 410 interacted or with content
items with which users in the target group 410 interacted. Hence,
users from the cluster group 420 identified as associated with the
specific topic are subsequently identified as having a
characteristic satisfying targeting criteria identifying the
specific topic, so content items or ad requests including the
specific topic in targeting criteria may be eligible for
presentation to the users identified from the cluster group 420.
Accordingly, the social networking system 140 includes users in the
cluster group 420 having at least a threshold measure of similarity
to users who previously interacted with content items that included
the specific topic in their targeting criteria or with
advertisements included in ad requests that included the specific
topic in their targeting criteria.
SUMMARY
[0050] The foregoing description of embodiments has been presented
for the purpose of illustration; it is not intended to be
exhaustive or to limit the patent rights to the precise forms
disclosed. Persons skilled in the relevant art can appreciate that
many modifications and variations are possible in light of the
above disclosure.
[0051] Some portions of this description describe embodiments in
terms of algorithms and symbolic representations of operations on
information. These algorithmic descriptions and representations are
commonly used by those skilled in the data processing arts to
convey the substance of their work effectively to others skilled in
the art. These operations, while described functionally,
computationally, or logically, are understood to be implemented by
computer programs or equivalent electrical circuits, microcode, or
the like. Furthermore, it has also proven convenient at times, to
refer to these arrangements of operations as modules, without loss
of generality. The described operations and their associated
modules may be embodied in software, firmware, hardware, or any
combinations thereof.
[0052] Any of the steps, operations, or processes described herein
may be performed or implemented with one or more hardware or
software modules, alone or in combination with other devices. In
one embodiment, a software module is implemented with a computer
program product comprising a computer-readable medium containing
computer program code, which can be executed by a computer
processor for performing any or all of the steps, operations, or
processes described.
[0053] Embodiments may also relate to an apparatus for performing
the operations herein. This apparatus may be specially constructed
for the required purposes, and/or it may comprise a general-purpose
computing device selectively activated or reconfigured by a
computer program stored in the computer. Such a computer program
may be stored in a non-transitory, tangible computer readable
storage medium, or any type of media suitable for storing
electronic instructions, which may be coupled to a computer system
bus. Furthermore, any computing systems referred to in the
specification may include a single processor or may be
architectures employing multiple processor designs for increased
computing capability.
[0054] Embodiments may also relate to a product that is produced by
a computing process described herein. Such a product may comprise
information resulting from a computing process, where the
information is stored on a non-transitory, tangible computer
readable storage medium and may include any embodiment of a
computer program product or other data combination described
herein.
[0055] Finally, the language used in the specification 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 patent rights be limited not by this detailed description,
but rather by any claims that issue on an application based hereon.
Accordingly, the disclosure of embodiments is intended to be
illustrative, but not limiting, of the scope of the patent rights,
which is set forth in the following claims.
* * * * *