U.S. patent application number 15/442069 was filed with the patent office on 2019-07-11 for per-article personalized models for recommending content email digests with personalized candidate article pools.
The applicant listed for this patent is LinkedIn Corporation. Invention is credited to Ajith Muralidharan, Ankan Saha.
Application Number | 20190213501 15/442069 |
Document ID | / |
Family ID | 59772744 |
Filed Date | 2019-07-11 |
United States Patent
Application |
20190213501 |
Kind Code |
A9 |
Saha; Ankan ; et
al. |
July 11, 2019 |
PER-ARTICLE PERSONALIZED MODELS FOR RECOMMENDING CONTENT EMAIL
DIGESTS WITH PERSONALIZED CANDIDATE ARTICLE POOLS
Abstract
A system, a machine-readable storage medium storing
instructions, and a computer-implemented method as described herein
are directed to a Personalized Article Engine that generates
respective prediction models for each article in a plurality of
candidate articles in a social network system. The Personalized.
Article Engine generates a respective article score according to
each article's prediction model and at least one feature of a
target member account. The Personalized Article Engine generates a
plurality of output scores based on combining each respective
article score with a corresponding article's global model score.
The Personalized Article Engine ranks the output scores to identify
a subset of candidate articles relevant to the target member
account.
Inventors: |
Saha; Ankan; (San Francisco,
CA) ; Muralidharan; Ajith; (Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
LinkedIn Corporation |
Sunnyvale |
CA |
US |
|
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20180060756 A1 |
March 1, 2018 |
|
|
Family ID: |
59772744 |
Appl. No.: |
15/442069 |
Filed: |
February 24, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62378674 |
Aug 23, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 5/022 20130101;
G06N 5/04 20130101; G06Q 50/01 20130101; G06F 16/248 20190101; G06N
20/00 20190101; H04L 67/42 20130101; G16H 10/60 20180101; G16H
70/60 20180101; G16H 80/00 20180101; G06F 16/256 20190101; H04L
67/306 20130101; G06F 16/2471 20190101; H04L 67/10 20130101 |
International
Class: |
G06N 99/00 20060101
G06N099/00; G06N 5/04 20060101 G06N005/04; H04L 29/08 20060101
H04L029/08 |
Claims
1. A computer system, comprising: a processor; a memory device
holding at least one instruction set executable on the processor to
cause the computer system to perform operations comprising:
generating respective prediction models for each article in a
plurality of candidate articles in a social network system;
generating a respective article score according to each article's
prediction model and at least one feature of a target member
account; generating a plurality of output scores based on combining
each respective article score with a corresponding article's global
model score; and ranking the output scores to identify a subset of
candidate articles relevant to the target member account.
2. The computer system as in claim 1, wherein generating respective
prediction models for each article in a plurality of candidate
articles in a social network systems comprises: training a first
prediction model for a first article from the plurality of
candidate articles in the social network system based on a
plurality of features of each member account that has interacted
with the first article in the social network system; and training a
second prediction model for a second article from the plurality of
candidate articles based on a plurality of features of each member
account that has interacted with the second article in the social
network system.
3. The computer system of claim 2, wherein a member account that
has interacted with a respective candidate article comprises a
member account that has performed any of the following member
account actions in the social network service: access the
respective candidate article, like the respective candidate
article, post a comment on the respective candidate article, share
the respective candidate article, and post the respective candidate
article.
4. The computer system of claim 2. wherein each feature of a
respective member account is based on at least any one of the
following types of member account profile data: one or more
industry descriptors, one or more job title descriptors, one or
more employer company descriptors, one or more educational
institution descriptors, one or more field of study descriptors,
one or more geographic area descriptors and one or more
professional level of experience indicators.
5. The computer system of claim 2, wherein generating a respective
article score according to each article's prediction model and at
least one feature of a target member account the first and the
second prediction models comprises: wherein the first and the
second prediction models are logistic regression models; and
assembling a first vector according to a first set of encoded
instructions representative of the first prediction model and each
regression coefficient of the first prediction model; and
assembling a second vector according to a second set of encoded
instructions representative of the second prediction model and each
regression coefficient of the second prediction model.
6. The computer system of claim 5, comprising: generating a first
article score for the first article according to a plurality of
features of the target member account and the first vector of the
first prediction model; and generating a second article score for
the second article according to the plurality of features of the
target member account and the first vector of the first prediction
model.
7. The computer system of claim 1, further comprising: wherein
generating respective prediction models for each article in a
plurality of candidate articles in a social network system
comprises: storing, in a prediction model database, each respective
prediction model for a given article in the plurality of candidate
articles; wherein generating a respective article score comprises:
for each respective article score: accessing in the prediction
model database a respective prediction model; and accessing a
member account database, wherein a portion of the member account
database includes at least a portion of member account profile data
of the target member account.
8. A non-transitory computer-readable medium storing executable
instructions thereon, which, when executed by a processor, cause
the processor to perform operations including: generating
respective prediction models for each article in a plurality of
candidate articles in a social network system; generating a
respective article score according to each article's prediction
model and at least one feature of a target member account;
generating a plurality of output scores based on combining each
respective article score with a corresponding article's global
model score; and ranking the output scores to identify a subset of
candidate articles relevant to the target member account.
9. The non-transitory computer-readable medium as in claim 8,
wherein generating respective prediction models for each article in
a plurality of candidate articles in a social network systems
comprises: training a first prediction model for a first article
from the plurality of candidate articles in the social network
system based on a plurality of features of each member account that
has interacted with the first article in the social network system;
and training a second prediction model for a second article from
the plurality of candidate articles based on a plurality of
features of each member account that has interacted with the second
article in the social network system.
10. The non-transitory computer-readable medium of claim 9, wherein
a member account that has interacted with a respective candidate
article comprises a member account that has performed any of the
following member account actions in the social network service:
access the respective candidate article, like the respective
candidate article, post a comment on the respective candidate
article, share the respective candidate article, and post the
respective candidate article.
11. The non-transitory computer-readable medium of claim 9, wherein
each feature of a respective member account is based on at least
any one of the following types of member account profile data: one
or more industry descriptors, one or more job title descriptors,
one or more employer company descriptors, one or more educational
institution descriptors, one or more field of study descriptors,
one or more geographic area descriptors and one or more
professional level of experience indicators.
12. The non-transitory computer-readable medium of claim 9, wherein
generating a respective article score according to each article's
prediction model and at least one feature of a target member
account the first and the second prediction models comprises:
wherein the first and the second prediction models are logistic
regression models; and assembling a first vector according to a
first set of encoded instructions representative of the first
prediction model and each regression coefficient of the first
prediction model; and assembling a second vector according to a
second set of encoded instructions representative of the second
prediction model and each regression coefficient of the second
prediction model.
13. The non-transitory computer-readable medium of claim 12,
comprising: generating a first article score for the first article
according to a plurality of features of the target member account
and the first vector of the first prediction model; and generating
a second article score for the second article according to the
plurality of features of the target member account and the first
vector of the first prediction model.
14. The non-transitory computer-readable medium of claim 8, further
comprising: wherein generating respective prediction models for
each article in a plurality of candidate articles in a social
network system comprises: storing, in a prediction model database,
each respective prediction model for a given article in the
plurality of candidate articles; wherein generating a respective
article score comprises: for each respective article score:
accessing in the prediction model database a respective prediction
model; and accessing a member account database, wherein a portion
of the member account database includes at least a portion of
member account profile data of the target member account.
15. A method comprising: generating, via at least one hardware
processor, respective prediction models for each article in a
plurality of candidate articles in a social network system;
generating a respective article score according to each article's
prediction model and at least one feature of a target member
account; generating a plurality of output scores based on combining
each respective article score with a corresponding article's global
model score; and ranking the output scores to identify a subset of
candidate articles relevant to the target member account.
16. The method as in claim 15, wherein generating respective
prediction models for each article in a plurality of candidate
articles in a social network systems comprises: training a first
prediction model for a first article from the plurality of
candidate articles in the social network system based on a
plurality of features of each member account that has interacted
with the first article in the social network system; and training a
second prediction model for a second article from the plurality of
candidate articles based on a plurality of features of each member
account that has interacted with the second article in the social
network system.
17. The method of claim 16, wherein a member account that has
interacted with a respective candidate article comprises a member
account that has performed any of the following member account
actions in the social network service: access the respective
candidate article, like the respective candidate article, post a
comment on the respective candidate article, share the respective
candidate article, and post the respective candidate article.
18. The method of claim 16, wherein each feature of a respective
member account is based on at least any one of the following types
of member account profile data: one or more industry descriptors,
one or more job title descriptors, one or more employer company
descriptors, one or more educational institution descriptors, one
or more field of study descriptors, one or more geographic area
descriptors and one or more professional level of experience
indicators.
19. The method of claim 16, wherein generating a respective article
score according to each article's prediction model and at least one
feature of a target member account the first and the second
prediction models comprises: wherein the first and the second
prediction models are logistic regression models; and assembling a
first vector according to a first set of encoded instructions
representative of the first prediction model and each regression
coefficient of the first prediction model; and assembling a second
vector according to a second set of encoded instructions
representative of the second prediction model and each regression
coefficient of the second prediction model.
20. The method of claim 19, comprising: generating a first article
score for the first article according to a plurality of features of
the target member account and the first vector of the first
prediction model; and generating a second article score for the
second article according to the plurality of features of the target
member account and the first vector of the first prediction model.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority to U.S.
Provisional Patent Application entitled "PER-ARTICLE PERSONALIZED
MODELS FOR RECOMMENDING CONTENT EMAIL DIGESTS WITH PERSONALIZED
CANDIDATE ARTICLE POOLS", Ser. No. 62/378,674, filed Aug. 23, 2016,
which is hereby incorporated herein by reference in its
entirety.
[0002] This application is related to U.S. patent application
entitled "PER-ARTICLE PERSONALIZED MODEL FEATURE TRANSFORMATION",
Attorney Docket No. 3080.I47US1, which is hereby incorporated
herein by reference in its entirety.
TECHNICAL FIELD
[0003] The subject matter disclosed herein generally relates to the
technical field of special-purpose machines that facilitate
determining relevance of content, including software-configured
computerized variants of such special-purpose machines and
improvements to such variants, and to the technologies by which
such special-purpose machines become improved compared to other
special-purpose machines that facilitate determining relevance of
content.
BACKGROUND
[0004] A social networking service is a computer- or web-based
application that enables users to establish links or connections
with persons for the purpose of sharing information with one
another. Some social networking services aim to enable friends and
family to communicate with one another, while others are
specifically directed to business users with a goal of enabling the
sharing of business information. For purposes of the present
disclosure, the terms "social network" and "social networking
service" are used in a broad sense and are meant to encompass
services aimed at connecting friends and family (often referred to
simply as "social networks"), as well as services that are
specifically directed to enabling business people to connect and
share business information (also commonly referred to as "social
networks" but sometimes referred to as "business networks").
[0005] With many social networking services, members are prompted
to provide a variety of personal information, which may be
displayed in a member's personal web page. Such information is
commonly referred to as personal profile information, or simply
"profile information", and when shown collectively, it is commonly
referred to as a member's profile. For example, with some of the
many social networking services in use today, the personal
information that is commonly requested and displayed includes a
member's age, gender, interests, contact information, home town,
address, the name of the member's spouse and/or family members, and
so forth. With certain social networking services, such as some
business networking services, a member's personal information may
include information commonly included in a professional resume or
curriculum vitae, such as information about a person's education,
employment history, skills, professional organizations, and so on.
With some social networking services, a member's profile may be
viewable to the public by default, or alternatively, the member may
specify that only some portion of the profile is to be public by
default. Accordingly, many social networking services serve as a
sort of directory of people to be searched and browsed.
DESCRIPTION OF THE DRAWINGS
[0006] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings in
which:
[0007] FIG, 1 is a block diagram illustrating a client-server
system, in accordance with an example embodiment;
[0008] FIG. 2 is a block diagram showing functional components of a
professional social network within a networked system, in
accordance with an example embodiment;
[0009] FIG. 3 is a block diagram showing example components of a
Personalized Article Engine, according to some embodiments;
[0010] FIG. 4 is a block diagram showing example data flow of a
Personalized Article Engine, according to some embodiments;
[0011] FIG. 5 is a block diagram showing example data flow of a
Personalized Article Engine, according to some embodiments;
[0012] FIG. 6 is a flowchart illustrating an example method for
generating a respective score according to a prediction model that
corresponds with a particular article according to an example
embodiment;
[0013] FIG. 7 is a block diagram of an example computer system on
which methodologies described herein may be executed, in accordance
with an example embodiment.
DETAILED DESCRIPTION
[0014] The present disclosure describes methods and systems for
determining relevance of content in social network service (also
referred to herein as a "professional social network" or "social
network"). In the following description, for purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the various aspects of
different embodiments of the present invention. It will be evident,
however, to one skilled in the art, that the present invention may
be practiced without all of the specific details.
[0015] A system, a machine-readable storage medium storing
instructions, and a computer-implemented method as described herein
are directed to a Personalized Article Engine that generates
respective prediction models for each article in a plurality of
candidate articles in a social network system. The Personalized
Article Engine generates a respective article score according to
each article's prediction model and at least one feature of a
target member account. The Personalized Article Engine generates a
plurality of output scores based on combining each respective
article score with a corresponding article's global model score.
The Personalized Article Engine ranks the output scores to identify
a subset of candidate articles relevant to the target member
account.
[0016] The Personalized Article Engine improves the performance of
a special-purpose computer system by more efficiently and
effectively identifying relevant content in a social network system
that may include millions of member accounts and millions of
various types of content.
[0017] According to exemplary embodiments, the Personalized Article
Engine builds and trains a prediction model for each article in a
plurality of articles. Each prediction model for an article can be
a logistic regression model with features identified by profile
data of those member accounts that have interacted with the
corresponding article. Each prediction model generates a score for
its corresponding article based on a dot product of a regression
coefficient vector and a vector based on features of a target
member account. For example, a member feature vector is assembled
to represent prediction model features present in the target member
account and a prediction model vector based on prediction model
coefficients that correspond to those present features. A dot
product of the member feature vector and the prediction model
vector is calculated to generate an output score. The output score
can be added to a global model score to determine an overall score
for an article.
[0018] The global model has a plurality of features and
coefficients that can determine relevance of a given article to a
target member account. The global model can be a logistic
regression model that includes a plurality of member features and a
plurality of article features. In the global model, a feature
vector is assembled based on present global model features of the
target member account and a given article and a global vector is
assembled based on global model coefficients that correspond to
those present features. A global model score generated by the
global model is based on a dot product of the feature vector and
the global vector. The global model score represents a generalized
score of the given article's relevance to the target member
account. Each article is ranked according to its corresponding
overall score (i.e. overall output score for an article for a
target member account=article's global model score+score calculated
by the prediction model that corresponds with the article).
[0019] A subset of the ranked articles (such as, for example, the
top five articles) are selected for a digest message to be sent to
the target member account. The digest message includes a listing of
the top five articles and provides access to each of the top five
articles. As such, the digest message facilitates engagement and
activity of the target member account in the social network service
by recommending highly relevant content that would be of interest
to the target member account.
[0020] According to various embodiments, the global model and each
prediction model of the Personalized Article Engine may be executed
for the purposes of both off-line training for generating,
training, and refining of one or more of the prediction models.
According to various embodiments, the Personalized Article Engine
builds and trains the global model and a prediction model for each
article in a plurality of article. The global model can be a
logistic regression model with features based on member account
attribute types and article attribute types. Each prediction model
for an article can be a logistic regression model with features
identified by profile data of those member accounts that have
interacted with the corresponding article. Each prediction model
generates a score for its corresponding article based on a dot
product of a regression coefficient vector and a vector based on
features of a target member account.
[0021] Various example embodiments further include encoded
instructions that comprise operations to generate a user
interface(s) and various user interface elements. The user
interface and the various user interface elements can be
representative of any of the operations, data, prediction models,
output, pre-defined features, identified features, coefficients,
member accounts, notifications, profile data, articles, one or more
type of member account interactions with articles, messages and
notifications as described herein. In addition, the user interface
and various user interface elements are generated by the
Personalized Article Engine for display on a computing device, a
server computing device, a mobile computing device, etc.
[0022] As described in various embodiments, the Personalized
Article Engine may be a configuration-driven system for building,
training, and deploying prediction models for determining relevance
of articles for a target member account. In particular, the
operation of the Personalized Article Engine is completely
configurable and customizable by a user through a user-supplied
configuration file such as a JavaScript Object Notation (JSON),
eXtensible Markup Language (XML) file, etc.
[0023] For example, each module in the Personalized Article Engine
may have text associated with it in a configuration file(s) that
describes how the module is configured, the inputs to the module,
the operations to be performed by module on the inputs, the outputs
from the module, and so on. Accordingly, the user may rearrange the
way these modules are connected together as well as the rules that
the various modules use to perform various operations. Thus,
whereas conventional prediction modelling is often performed in a
fairly ad hoc and code driven manner, the modules of the
Personalized Article Engine may be configured in a modular and
reusable fashion, to enable more efficient prediction
modelling.
[0024] Turning to FIG. 1 is a block diagram illustrating a
client-server system, in accordance with an example embodiment. A
networked system 102 provides server-side functionality via a
network 104 (e.g., the Internet or Wide Area Network (WAN)) to one
or more clients. FIG. 1 illustrates, for example, a web client 106
(e.g., a browser) and a programmatic client 108 executing on
respective client machines 110 and 112.
[0025] An Application Program Interface (API) server 114 and a web
server 116 are coupled to, and provide programmatic and web
interfaces respectively to, one or more application servers 118.
The application servers 118 host one or more applications 120. The
application servers 118 are, in turn, shown to be coupled to one or
more database servers 124 that facilitate access to one or more
databases 126. While the applications 120 are shown in FIG. 1 to
form part of the networked system 102, it will be appreciated that,
in alternative embodiments, the applications 120 may form part of a
service that is separate and distinct from the networked system
102.
[0026] Further, while the system 100 shown in FIG. 1 employs a
client-server architecture, the present disclosure is of course not
limited to such an architecture, and could equally well find
application in a distributed, or peer-to-peer, architecture system,
for example. The various applications 120 could also be implemented
as standalone software programs, which do not necessarily have
networking capabilities.
[0027] The web client 106 accesses the various applications 120 via
the web interface supported by the web server 116. Similarly, the
programmatic client 108 accesses the various services and functions
provided by the applications 120 via the programmatic interface
provided by the API server 114.
[0028] FIG. 1 also illustrates a third party application 128,
executing on a third party server machine 130, as having
programmatic access to the networked system 102 via the
programmatic interface provided by the API server 114. For example,
the third party application 128 may, utilizing information
retrieved from the networked system 102, support one or more
features or functions on a website hosted by the third party. The
third party website may, for example, provide one or more functions
that are supported by the relevant applications of the networked
system 102. In some embodiments, the networked system 102 may
comprise functional components of a professional social
network.
[0029] FIG. 2 is a block diagram showing functional components of a
professional social network within the networked system 102, in
accordance with an example embodiment.
[0030] As shown in FIG. 2, the professional social network may be
based on a three-tiered architecture, consisting of a front-end
layer 201, an application logic layer 203, and a data layer 205. In
some embodiments, the modules, systems, and/or engines shown in
FIG. 2 represent a set of executable software instructions and the
corresponding hardware (e.g., memory and processor) for executing
the instructions. To avoid obscuring the inventive subject matter
with unnecessary detail, various functional modules and engines
that are not germane to conveying an understanding of the inventive
subject matter have been omitted from FIG. 2. However, one skilled
in the art will readily recognize that various additional
functional modules and engines may be used with a professional
social network, such as that illustrated in FIG. 2. to facilitate
additional functionality that is not specifically described herein.
Furthermore, the various functional modules and engines depicted in
FIG. 2 may reside on a single server computer, or may be
distributed across several server computers in various
arrangements. Moreover, although a professional social network is
depicted in FIG. 2 as a three-tiered architecture, the inventive
subject matter is by no means limited to such architecture. It is
contemplated that other types of architecture are within the scope
of the present disclosure.
[0031] As shown in FIG, 2, in some embodiments, the front-end layer
201 comprises a user interface module (e.g., a web server) 202,
which receives requests and inputs from various client-computing
devices, and communicates appropriate responses to the requesting
client devices. For example, the user interface module(s) 202 may
receive requests in the form of Hypertext Transport Protocol (HTTP)
requests, or other web-based, application programming interface
(API) requests.
[0032] In some embodiments, the application logic layer 203
includes various application server modules 204, which, in
conjunction with the user interface module(s) 202, generates
various user interfaces (e.g., web pages) with data retrieved from
various data sources in the data layer 205. In some embodiments,
individual application server modules 204 are used to implement the
functionality associated with various services and features of the
professional social network. For instance, the ability of an
organization to establish a presence in a social graph of the
social network service, including the ability to establish a
customized web page on behalf of an organization, and to publish
messages or status updates on behalf of an organization, may be
services implemented in independent application server modules 204.
Similarly, a variety of other applications or services that are
made available to members of the social network service may be
embodied in their own application server modules 204.
[0033] As shown in FIG. 2, the data layer 205 may include several
databases, such as a database 210 for storing profile data 216,
including both member profile attribute data as well as profile
attribute data for various organizations. Consistent with some
embodiments, when a person initially registers to become a member
of the professional social network, the person will be prompted to
provide some profile attribute data such as, such as his or her
name, age (e.g., birthdate), gender, interests, contact
information, home town, address, the names of the member's spouse
and/or family members, educational background (e.g., schools,
majors, matriculation and/or graduation dates, etc.), employment
history, skills, professional organizations, and so on. This
information may be stored, for example, in the database 210.
Similarly, when a representative of an organization initially
registers the organization with the professional social network the
representative may be prompted to provide certain information about
the organization. This information may be stored, for example, in
the database 210, or another database (not shown). With some
embodiments, the profile data 216 may be processed (e.g., in the
background or offline) to generate various derived profile data.
For example, if a member has provided information about various job
titles the member has held with the same company or different
companies, and for how long, this information can be used to infer
or derive a member profile attribute indicating the member's
overall seniority level, or a seniority level within a particular
company. With some embodiments, importing or otherwise accessing
data from one or more externally hosted data sources may enhance
profile data 216 for both members and organizations. For instance,
with companies in particular, financial data may be imported from
one or more external data sources, and made part of a company's
profile.
[0034] The profile data 216 may also include information regarding
settings for members of the professional social network. These
settings may comprise various categories, including, but not
limited to, privacy and communications. Each category may have its
own set of settings that a member may control.
[0035] Once registered, a member may invite other members, or be
invited by other members, to connect via the professional social
network. A "connection" may require a bi-lateral agreement by the
members, such that both members acknowledge the establishment of
the connection. Similarly, with some embodiments, a member may
elect to "follow" another member. In contrast to establishing a
connection, the concept of "following" another member typically is
a unilateral operation, and at least with some embodiments, does
not require acknowledgement or approval by the member that is being
followed. When one member follows another, the member who is
following may receive status updates or other messages published by
the member being followed, or relating to various activities
undertaken by the member being followed. Similarly, when a member
follows an organization, the member becomes eligible to receive
messages or status updates published on behalf of the organization.
For instance, messages or status updates published on behalf of an
organization that a member is following will appear in the member's
personalized data feed or content stream. In any case, the various
associations and relationships that the members establish with
other members, or with other entities and objects, may be stored
and maintained as social graph data within a social graph database
212.
[0036] The professional social network may provide a broad range of
other applications and services that allow members the opportunity
to share and receive information, often customized to the interests
of the member. For example, with some embodiments, the professional
social network may include a photo sharing application that allows
members to upload and share photos with other members. With some
embodiments, members may be able to self-organize into groups, or
interest groups, organized around a subject matter or topic of
interest. With some embodiments, the professional social network
may host various job listings providing details of job openings
with various organizations.
[0037] In some embodiments, the professional social network
provides an application programming interface (API) module via
which third-party applications can access various services and data
provided by the professional social network. For example, using an
API, a third-party application may provide a user interface and
logic that enables an authorized representative of an organization
to publish messages from a third-party application to a content
hosting platform of the professional social network that
facilitates presentation of activity or content streams maintained
and presented by the professional social network. Such third-party
applications may be browser-based applications, or may be operating
system-specific. In particular, some third-party applications may
reside and execute on one or more mobile devices (e.g., a
smartphone, or tablet computing devices) having a mobile operating
system.
[0038] The data in the data layer 205 may be accessed, used, and
adjusted by a Personalized Article Engine 206 as will be described
in more detail below in conjunction with FIGS. 3-7, Although the
Personalized Article Engine 206 is referred to herein as being used
in the context of a professional social network, it is contemplated
that it may also be employed in the context of any website or
online services, including, but not limited to, content sharing
sites (e.g., photo- or video-sharing sites) and any other online
services that allow users to have a profile and present themselves
or content to other users. Additionally, although features of the
present disclosure are referred to herein as being used or
presented in the context of a web page, it is contemplated that any
user interface view (e.g., a user interface on a mobile device or
on desktop software) is within the scope of the present
disclosure.
[0039] The data layer 205 further includes a database 214 that
includes training data 214 for generating one or more prediction
models. Such training data 214 can be, for example, identifiers of
one or more member account that have interacted with an article(s).
The database 214 can further store one or more prediction
models.
[0040] FIG. 3 is a block diagram showing example components of a
Personalized Article Engine, according to some embodiments.
[0041] The input module 305 is a hardware-implemented module that
controls, manages and stores information related to any inputs from
one or more components of system 102 as illustrated in FIG. 1 and
FIG. 2. In various embodiments, the inputs include, in part, one or
more candidate articles, profile data of member accounts that have
interacted with the one or more candidate articles and profile data
of a target member account.
[0042] The output module 310 is a hardware-implemented module that
controls, manages and stores information related to which sends any
outputs to one or more components of system 100 of FIG. 1 (e.g.,
one or more client devices 110, 112, third party server 130, etc.).
In some embodiments, the output is a message or notification that
includes a digest (such as a listing) of one or more articles that
have scores that indicate a relevance to the target member
account.
[0043] The interacting account module 315 is a hardware implemented
module which manages, controls, stores, and accesses information
related to collecting identifications and profile data of each
member account that accesses each article in a plurality of
candidate articles.
[0044] The training module 320 is a hardware-implemented module
which manages, controls, stores, and accesses information related
to generating a prediction model for each article in a plurality of
candidate articles.
[0045] The scoring module 325 is a hardware-implemented module
which manages, controls, stores, and accesses information related
to calculating a score based on vectors assembled according to
encoded instructions of a prediction model.
[0046] The ranking module 330 is a hardware-implemented module
which manages, controls, stores, and accesses information related
to ranking scores produced as output from each prediction model for
each article. It is understood that a module can be a software
module, such as a set of instructions executable on one or more
hardware processors.
[0047] FIG. 4 is a block diagram showing example data flow of a
Personalized Article Engine, according to some embodiments. The
data flow may be implemented by one or more of the modules
illustrated in FIG. 3, and is discussed by way of reference
thereto.
[0048] The Personalized Article Engine 206 has access to a
plurality of candidate articles 400, 402, 404 in a social network
service. It is understood that there can be hundreds, thousands or
even millions of candidate articles in the social network service
to which the Personalized Article Engine 206 has access. The
Personalized Article Engine 206 further has access to profile data
of one or more member accounts 406, 408, 410 that have already
interacted with each of the articles 400, 402, 404. For example,
hundreds of member accounts have interacted with the articles 400,
402, 404. It is understood some member accounts have interacted
with all the articles 400, 402, 404, whereas some of the member
accounts may have only interacted with some or one of the articles
400, 402, 404.
[0049] The training module 320 of the Personalized Article Engine
206 accesses the profile data of the member accounts 406, 408, 410
to train a prediction model 412, 414, 416 for each article 400,
402, 404. That is, a first article 400 has a corresponding first
prediction model 412, a second article 402 has a corresponding
second prediction model 414, a third article 404 has a
corresponding third prediction model 416. The training module 320
may use any one of various known prediction modelling techniques to
perform a prediction modelling process to build and train each
prediction model 412, 414, 416. Each prediction model 412, 414, 416
returns a score for each corresponding article 400, 402, 404. Each
respective score representing a relevance of an article to a target
member account. Such relevance of an article can be indicative of a
probability of whether the target member account will access the
article given the profile data of the member accounts how have
already accessed the article.
[0050] According to various exemplary embodiments, the training
module 320 may perform the prediction modelling process based on a
statistics-based machine learning model such as a logistic
regression model. As understood by those skilled in the art,
logistic regression is an example of a statistics-based machine
learning technique that uses a logistic function. The logistic
function is based on a variable, referred to as a logit. The logit
is defined in terms of a set of regression coefficients of
corresponding independent predictor variables. Logistic regression
can be used to predict the probability of occurrence of an event
given a set of independent/predictor variables. The
independent/predictor variables of the logistic regression model
are the attributes represented by the assembled feature vectors
described throughout. The regression coefficients may be estimated
using maximum likelihood or learned through a supervised learning
technique from data collected (such as profile data of member
account 406, 408, 410) in logs or calculated from log data, as
described in more detail below. Accordingly, once the appropriate
regression coefficients (e.g., B) are determined, the features
included in the assembled feature vector may be input to the
logistic regression model in order to predict the probability that
the event Y occurs (where the event Y may be, for example, whether
a target member account would select to view a particular
article).
[0051] In other words, provided an assembled feature vector
including various features associated with a particular member
account, a particular content item, a particular context, and so
on, the assembled feature vector may be applied to a logistic
regression model to determine the probability that the particular
member account will respond to the particular content item in a
particular way (e.g., receipt of a mouse click, a request to
access, a user selection) given the particular context. Logistic
regression is well understood by those skilled in the art, and will
not be described in further detail herein, in order to avoid
occluding various aspects of this disclosure.
[0052] It is understood that the training module 320 may use
various other prediction modelling techniques understood by those
skilled in the art to predict whether a particular member account
will click on a particular content item in a particular context.
For example, other prediction modelling techniques may include
other machine learning models such as a Naive Bayes model, a
support vector machines (SVM) model, a decision trees model, and a
neural network model, all of which are understood by those skilled
in the art. Also, according to various exemplary embodiments, the
training module 320 may be used for the purposes of both off-line
training (for generating, training, and refining a prediction model
412, 414, 416) and online inferences (for predicting whether a
particular member will click on a particular content item given a
particular context, based on a prediction model that corresponds
with the particular content item).
[0053] FIG. 5 is a block diagram showing example data flow of a
Personalized Article Engine, according to some embodiments. The
data flow may be implemented by one or more of the modules
illustrated in FIG. 3 and is discussed by way of reference
thereto.
[0054] The Personalized Article Engine 206 accesses a plurality of
accounts that have interacted with a particular article 500. Each
account 502, 504, 506 has corresponding profile data 502-1, 504-1,
506-1. The training module 320 of the Personalized Article Engine
206 executes a logistic regression modelling process with respect
to the profile data 502-1, 504-1, 506-1. A feature identifier 325-1
identities the features of the prediction model 510 for the
particular article. It is understood that each feature is based on
one or more type(s) of attribute of profile data 502-1, 504-1,
506-1 that is statistically significant in determining whether a
given member account will want to access the particular article.
Such profile data can be, for example, descriptors of: any of a
plurality of types of industry, any of a plurality of types of
companies, any of a plurality of types of skills, any of a
plurality of types of fields of study, any of a plurality of types
of levels of professional experience, any of a plurality of types
of schools, and/or any of a plurality of types of job titles. A
coefficient generator 325-2 calculates a regression coefficient for
each of the identified features according to the logistic
regression modelling process.
[0055] The training module 320 generates encoded instructions 512
representative of the prediction model 510 for the particular
article. The Personalized Article Engine 206 assembles vectors
according to the encoded instructions, The encoded instructions
indicates a vector position for each type of feature. For example,
the Personalized Article Engine 206 assembles a coefficient vector
516 based on the regression coefficients. Each regression
coefficient is positioned in the coefficient vector 516 at the
vector position for its corresponding feature. For example, a first
regression coefficient of a first type of feature is placed in the
coefficient vector 516 at the first type of feature's assigned
vector position. A second regression coefficient of a second type
of feature is placed in the coefficient vector 516 at the second
type of feature's assigned vector position.
[0056] The Personalized Article Engine 206 assembles a target
feature vector 514 based on the profile data 508-1 of a target
member account 508. For example, if the first type of feature is
present in the profile data 508-1, then a first value is placed in
the target feature vector 514 at the first type of feature's
assigned vector position. The first value can be a "1" to represent
presence of the first type of feature in the profile data 508-1. In
other embodiments, the first value can be a pre-defined value for
the first type of feature in the profile data 508-1. For example,
if the first type of feature corresponds to a geographic region
indicator and "San Francisco Bay Area" is pre-assigned a "0.8"
value, then "0.8" is placed in the target feature vector 514 at the
first type of feature's assigned vector position if the profile
data 508-1 of the target member account includes the "San Francisco
Bay Area" geographic region indicator. The Personalized Article
Engine 206 generates a score based on a dot product of the target
feature vector 514 and the coefficient vector 516.
[0057] FIG. 6 is a flowchart 600 illustrating an example method for
generating a respective score according to a prediction model that
corresponds with a particular article according to an example
embodiment. The method of FIG. 6 may be implemented by one or more
of the modules illustrated in FIG. 3 and is discussed by way of
reference thereto.
[0058] At operation 610, the Personalized Article Engine 206
generates respective prediction models for each article in a
plurality of candidate articles in a social network system.
[0059] According to exemplary embodiments, the Personalized Article
Engine 206 generates a prediction model for each candidate article
from a plurality of candidate articles in a social network system.
That is, if there are 100 articles, the Personalized Article Engine
206 trains 100 prediction models, with one prediction model for
each candidate article. If there are 1000 candidate articles, the
Personalized Article Engine trains 1000 prediction models. A
prediction model for a particular candidate article is trained
according to the features of the member accounts that have
interacted with that particular candidate article. Such features
can be, for example, any profile data of those member accounts,
such as: industry, company, skills, field of study, experience,
school, job title, etc. The Personalized Article Engine 206
generates each respective prediction model by storing features and
a coefficients in a data structure that represents the data model
(such as a logistic regression model) of the respective prediction
model. To execute the respective prediction model, the Personalized
Article Engine 206 accesses an instruction set(s) that simulates
data model calculations with respect to the features and the
coefficients stored in the data structure and profile data of an
input member account, such as a target member account.
[0060] In one embodiment, each candidate article prediction model
is a logistic regression model and will have regression
coefficients based on the features of the member accounts that have
already interacted with the corresponding candidate article. By
training a candidate article prediction model based on the member
accounts that have already interacted (e.g. accessed, viewed,
shared, liked, commented, posted) with the corresponding candidate
article, a more precise determination of relevance of the candidate
article can be determined with respect to the features of a target
member account--as opposed to relying solely on a generalized
prediction model for all candidate articles.
[0061] At operation 615, the Personalized Article Engine 206
generates a respective article score according to each prediction
model and at least one feature of a target member account.
[0062] According to exemplary embodiments, the Personalized Article
Engine 206 inputs features of the target member account into each
prediction model for each candidate article. For a particular
candidate article prediction model, the Personalized Article Engine
206 assembles a vector based on the regression coefficients
according to encoded instructions representative of that particular
candidate article prediction model. The Personalized Article Engine
206 scores the particular candidate article according to a dot
product of the target member account features and the assembled
vector. In other words, features of the particular candidate
article prediction model's present in the target member account's
profile data are detected. A target member account vector is
assembled representing the detected features. A coefficient vector
is assembled representing each regression coefficient of the
particular candidate article prediction. The Personalized Article
Engine 206 preforms such scoring according to each respective
candidate article model and ranks the candidate articles according
to their corresponding scores.
[0063] The Personalized Article Engine 206 generates a plurality of
output scores based on combining each respective article score with
a corresponding article's global model score. At operation 620, the
Personalized Article Engine 206 ranks output scores to identify a
subset of candidate articles relevant to the target member
account.
[0064] According to exemplary embodiments, the Personalized Article
Engine 206 selects--based on output scores--a subset of the ranked
candidate articles, such as the top three candidate articles or the
top 25% candidate articles. The Personalized Article Engine 206
includes the selected candidate articles in a message or
notification to be sent to the target member account. In various
embodiments, the message or notification is generated and sent to
the target member account on a daily, weekly or monthly basis. The
message or notification informs the target member account of
articles that are relevant to the target member account.
[0065] Certain embodiments are described herein as including logic
or a number of components, modules, or mechanisms. Modules may
constitute either software modules (e.g., code embodied on a
machine-readable medium or in a transmission signal) or hardware
modules. A hardware module is a tangible unit capable of performing
certain operations and may be configured or arranged in a certain
manner. In example embodiments, one or more computer systems (e.g.,
a standalone, client or server computer system) or one or more
hardware modules of a computer system (e.g., a processor or a group
of processors) may be configured by software (e.g., an application
or application portion) as a hardware module that operates to
perform certain operations as described herein.
[0066] In various embodiments, a hardware module may be implemented
mechanically or electronically. For example, a hardware module may
comprise dedicated circuitry or logic that is permanently
configured (e.g., as a special-purpose processor, such as a field
programmable gate array (FPGA) or an application-specific
integrated circuit (ASIC)) to perform certain operations. A
hardware module may also comprise programmable logic or circuitry
(e.g., as encompassed within a general-purpose processor or other
programmable processor) that is temporarily configured by software
to perform certain operations. It will be appreciated that the
decision to implement a hardware module mechanically, in dedicated
and permanently configured circuitry, or in temporarily configured
circuitry (e.g., configured by software) may be driven by cost and
time considerations.
[0067] Accordingly, the term "hardware module" should be understood
to encompass a tangible entity, be that an entity that is
physically constructed, permanently configured (e.g., hardwired) or
temporarily configured (e.g., programmed) to operate in a certain
manner and/or to perform certain operations described herein.
Considering embodiments in which hardware modules are temporarily
configured (e.g., programmed), each of the hardware modules need
not be configured or instantiated at any one instance in time. For
example, where the hardware modules comprise a general-purpose
processor configured using software, the general-purpose processor
may be configured as respective different hardware modules at
different times. Software may accordingly configure a processor,
for example, to constitute a particular hardware module at one
instance of time and to constitute a different hardware module at a
different instance of time.
[0068] Hardware modules can provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple of such hardware modules exist
contemporaneously, communications may be achieved through signal
transmission (e.g., over appropriate circuits and buses) that
connect the hardware modules. In embodiments in which multiple
hardware modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation, and store
the output of that operation in a memory device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices, and can operate on a resource (e.g.,
a collection of information).
[0069] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules that operate to perform one or more
operations or functions. The modules referred to herein may, in
some example embodiments, comprise processor-implemented
modules.
[0070] Similarly, the methods described herein may be at least
partially processor-implemented. For example, at least some of the
operations of a method may be performed by one or more processors
or processor-implemented modules. The performance of certain of the
operations may be distributed among the one or more processors, not
only residing within a single machine, but deployed across a number
of machines. In some example embodiments, the processor or
processors may be located in a single location (e.g., within a home
environment, an office environment or as a server farm), while in
other embodiments the processors may be distributed across a number
of locations.
[0071] The one or more processors may also operate to support
performance of the relevant operations in a "cloud computing"
environment or as a "software as a service" (SaaS). For example, at
least some of the operations may be performed by a group of
computers (as examples of machines including processors), these
operations being accessible via a network (e.g., the Internet) and
via one or more appropriate interfaces (e.g., application program
interfaces (APIs)).
[0072] Example embodiments may be implemented in digital electronic
circuitry, or in computer hardware, firmware, software, or in
combinations of them. Example embodiments may be implemented using
a computer program product, e.g., a computer program tangibly
embodied in an information carrier, e.g., in a machine-readable
medium for execution by, or to control the operation of, data
processing apparatus, e.g., a programmable processor, a computer,
or multiple computers.
[0073] A computer program can be written in any form of programming
language, including compiled or interpreted languages, and it can
be deployed in any form, including as a stand-alone program or as a
module, subroutine, or other unit suitable for use in a computing
environment. A computer program can be deployed to be executed on
one computer or on multiple computers at one site or distributed
across multiple sites and interconnected by a communication
network.
[0074] In example embodiments, operations may be performed by one
or more programmable processors executing a computer program to
perform functions by operating on input data and generating output.
Method operations can also be performed by, and apparatus of
example embodiments may be implemented as, special purpose logic
circuitry (e.g., a FPGA or an ASIC).
[0075] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In embodiments deploying
a programmable computing system, it will be appreciated that that
both hardware and software architectures require consideration.
Specifically, it will be appreciated that the choice of whether to
implement certain functionality in permanently configured hardware
(e.g., an ASIC), in temporarily configured hardware (e.g., a
combination of software and a programmable processor), or a
combination of permanently and temporarily configured hardware may
be a design choice. Below are set out hardware (e.g., machine) and
software architectures that may be deployed, in various example
embodiments.
[0076] FIG. 7 is a block diagram of an example computer system 700
on which methodologies described herein may be executed, in
accordance with an example embodiment. In alternative embodiments,
the machine operates as a standalone device or may be connected
(e.g., networked) to other machines. In a networked deployment, the
machine may operate in the capacity of a server or a client machine
in server-client network environment, or as a peer machine in a
peer-to-peer (or distributed) network environment. The machine may
be a personal computer (PC), a tablet PC, a set-top box (STB), a
Personal Digital Assistant (PDA), a cellular telephone, a web
appliance, a network router, switch or bridge, or any machine
capable of executing instructions (sequential or otherwise) that
specify actions to be taken by that machine. Further, while only a
single machine is illustrated, the term "machine" shall also be
taken to include any collection of machines that individually or
jointly execute a set (or multiple sets) of instructions to perform
any one or more of the methodologies discussed herein.
[0077] Example computer system 700 includes a processor 702 (e.g.,
a central processing unit (CPU), a graphics processing unit (GPU)
or both), a main memory 704, and a static memory 706, which
communicate with each other via a bus 708. Computer system 700 may
further include a video display device 710 (e.g., a liquid crystal
display (LCD) or a cathode ray tube (CRT)). Computer system 700
also includes an alphanumeric input device 712 (e.g., a keyboard),
a user interface (UI) navigation device 714 (e.g., a mouse or touch
sensitive display), a disk drive unit 716, a signal generation
device 718 (e.g., a speaker) and a network interface device
720.
[0078] Disk drive unit 716 includes a machine-readable medium 722
on which is stored one or more sets of instructions and data
structures (e.g., software) 724 embodying or utilized by any one or
more of the methodologies or functions described herein.
Instructions 724 may also reside, completely or at least partially,
within main memory 704, within static memory 706, and/or within
processor 702 during execution thereof by computer system 700, main
memory 704 and processor 702 also constituting machine-readable
media.
[0079] While machine-readable medium 722 is shown in an example
embodiment to be a single medium, the term "machine-readable
medium" may include a single medium or multiple media (e.g., a
centralized or distributed database, and/or associated caches and
servers) that store the one or more instructions or data
structures. The term "machine-readable medium" shall also be taken
to include any tangible medium that is capable of storing, encoding
or carrying instructions for execution by the machine and that
cause the machine to perform any one or more of the methodologies
of the present technology, or that is capable of storing, encoding
or carrying data structures utilized by or associated with such
instructions. The term "machine-readable medium" shall accordingly
be taken to include, but not be limited to, solid-state memories,
and optical and magnetic media. Specific examples of
machine-readable media include non-volatile memory, including by
way of example semiconductor memory devices, e.g., Erasable
Programmable Read-Only Memory (EPROM), Electrically Erasable
Programmable Read-Only Memory (EEPROM), and flash memory devices;
magnetic disks such as internal hard disks and removable disks;
magneto-optical disks; and CD-ROM and DVD-ROM disks.
[0080] Instructions 724 may further be transmitted or received over
a communications network 726 using a transmission medium.
Instructions 724 may be transmitted using network interface device
720 and any one of a number of well-known transfer protocols (e.g.,
HTTP). Examples of communication networks include a local area
network ("LAN"), a wide area network ("WAN"), the Internet, mobile
telephone networks, Plain Old Telephone (POTS) networks, and
wireless data networks (e.g., WiFi and WiMAX networks). The term
"transmission medium" shall be taken to include any intangible
medium that is capable of storing, encoding or carrying
instructions for execution by the machine, and includes digital or
analog communications signals or other intangible media to
facilitate communication of such software.
[0081] Although an embodiment has been described with reference to
specific example embodiments, it will be evident that various
modifications and changes may be made to these embodiments without
departing from the broader spirit and scope of the technology.
Accordingly, the specification and drawings are to be regarded in
an illustrative rather than a restrictive sense. The accompanying
drawings that form a part hereof, show by way of illustration, and
not of limitation, specific embodiments in which the subject matter
may be practiced. The embodiments illustrated are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed herein. Other embodiments may be utilized
and derived therefrom, such that structural and logical
substitutions and changes may be made without departing from the
scope of this disclosure. This Detailed Description, therefore, is
not to be taken in a limiting sense, and the scope of various
embodiments is defined only by the appended claims, along with the
full range of equivalents to which such claims are entitled.
[0082] Such embodiments of the inventive subject matter may be
referred to herein, individually and/or collectively, by the term
"invention" merely for convenience and without intending to
voluntarily limit the scope of this application to any single
invention or inventive concept if more than one is in fact
disclosed. Thus, although specific embodiments have been
illustrated and described herein, it should be appreciated that any
arrangement calculated to achieve the same purpose may be
substituted for the specific embodiments shown. This disclosure is
intended to cover any and all adaptations or variations of various
embodiments. Combinations of the above embodiments, and other
embodiments not specifically described herein, will be apparent to
those of skill in the art upon reviewing the above description.
* * * * *