U.S. patent application number 15/487015 was filed with the patent office on 2017-08-03 for learning a ranking model using interactions of a user with a jobs list.
The applicant listed for this patent is LinkedIn Corporation. Invention is credited to David Hardtke, Eric Huang, Xu Miao, Lijun Tang, Joel Daniel Young, Yitong Zhou.
Application Number | 20170221007 15/487015 |
Document ID | / |
Family ID | 57683243 |
Filed Date | 2017-08-03 |
United States Patent
Application |
20170221007 |
Kind Code |
A1 |
Tang; Lijun ; et
al. |
August 3, 2017 |
LEARNING A RANKING MODEL USING INTERACTIONS OF A USER WITH A JOBS
LIST
Abstract
Learning to rank modeling in the context of an on-line social
network is described. A learning to rank model can learn from
pairwise preference (e.g., job posting A is more relevant than job
posting B for a particular member profile) thus directly optimizing
for the rank order of job postings for each member profile. With
ranking position taken into consideration during training,
top-ranked job postings may be treated by a recommendation system
as being of more importance than lower-ranked job postings. In
addition, a learning to rank approach may also result in an equal
optimization across all member profiles and help minimize bias
towards those member profiles that have been paired with a larger
number of job postings.
Inventors: |
Tang; Lijun; (Mountain View,
CA) ; Huang; Eric; (San Francisco, CA) ; Miao;
Xu; (Sunnyvale, CA) ; Zhou; Yitong;
(Sunnyvale, CA) ; Hardtke; David; (Oakland,
CA) ; Young; Joel Daniel; (Milpitas, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
LinkedIn Corporation |
Sunnyvale |
CA |
US |
|
|
Family ID: |
57683243 |
Appl. No.: |
15/487015 |
Filed: |
April 13, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
14788711 |
Jun 30, 2015 |
9626654 |
|
|
15487015 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
G06F 16/24578 20190101; G06Q 50/01 20130101; H04L 67/306 20130101;
G06Q 10/1053 20130101; G06N 5/025 20130101; G06F 16/9535
20190101 |
International
Class: |
G06Q 10/10 20060101
G06Q010/10; H04L 29/08 20060101 H04L029/08; G06N 99/00 20060101
G06N099/00 |
Claims
1. A computer-implemented method comprising: collecting training
data, the training data comprising a plurality of job lists, each
job list from the plurality of job lists comprising respective
identifications of a plurality of job postings, each identification
of a job posting from the plurality of job postings assigned a
relevance label indicating a grade of relevance of that job posting
with respect to a member profile associated with that job list, the
plurality of job postings maintained by an on-line social network
system, the member profile being from a plurality of member
profiles representing respective members of the on-line social
network system. using at least one processor, learning a ranking
model using (1) relevance labels from the training data and (2)
rank scores calculated for (member profile, job posting) pairs from
the training data; accessing a recommended jobs list, the
recommended jobs list generated by a retrieval engine for a member
profile representing a member in the on-line social network system,
executing the ranking model to determine respective rank scores for
items in the recommended jobs list, an item in the recommended jobs
list representing a job posting maintained by the on-line social
network system; causing the items from the recommended jobs list to
be presented on a display device in an order based on the
determined respective rank scores.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of prior application Ser.
No. 14/788,711, filed on Jun. 30, 2015, which is incorporated by
reference herein in its entirety.
TECHNICAL FIELD
[0002] This application relates to the technical fields of software
and/or hardware technology and, in one example embodiment, to
learning to rank modeling in the context of on-line social network
data.
BACKGROUND
[0003] An on-line social network may be viewed as a platform to
connect people in virtual space. An on-line social network may be a
web-based platform, such as, e.g., a social networking web site,
and may be accessed by a use via a web browser or via a mobile
application provided on a mobile phone, a tablet, etc. An on-line
social network may be a business-focused social network that is
designed specifically for the business community, where registered
members establish and document networks of people they know and
trust professionally. Each registered member may be represented by
a member profile. A member profile may be include one or more web
pages, or a structured representation of the member's information
in XML (Extensible Markup Language), JSON (JavaScript Object
Notation), etc. A member's profile web page of a social networking
web site may emphasize employment history and education of the
associated member. An on-line social network may include one or
more components for matching member profiles with those job
postings that may be of interest to the associated member.
BRIEF DESCRIPTION OF DRAWINGS
[0004] Embodiments of the present invention are illustrated by way
of example and not limitation in the figures of the accompanying
drawings, in which like reference numbers indicate similar elements
and in which:
[0005] FIG. 1 is a diagrammatic representation of a network
environment within which an example method and system to utilize
learning to rank modeling in the context of an on-line social
network data may be implemented;
[0006] FIG. 2 is a diagram of an architecture for learning to rank
modeling in the context of an on-line social network data, in
accordance with one example embodiment;
[0007] FIG. 3 is an illustration of the use of decision trees as a
learning to rank algorithm, in accordance with one example
embodiment;
[0008] FIG. 4 is block diagram of a recommendation system, in
accordance with one example embodiment;
[0009] FIG. 5 is a flow chart of a method to utilize learning to
rank modeling in the context of an on-line social network data, in
accordance with an example embodiment; and
[0010] FIG. 6 is a diagrammatic representation of an example
machine in the form of a computer system within which a set of
instructions, for causing the machine to perform any one or more of
the methodologies discussed herein, may be executed.
DETAILED DESCRIPTION
[0011] Learning to rank modeling in the context of an on-line
social network is described. In the following description, for
purposes of explanation, numerous specific details are set forth in
order to provide a thorough understanding of an embodiment of the
present invention. It will be evident, however, to one skilled in
the art that the present invention may be practiced without these
specific details.
[0012] As used herein, the term "or" may be construed in either an
inclusive or exclusive sense. Similarly, the term "exemplary" is
merely to mean an example of something or an exemplar and not
necessarily a preferred or ideal means of accomplishing a goal.
Additionally, although various exemplary embodiments discussed
below may utilize Java-based servers and related environments, the
embodiments are given merely for clarity in disclosure. Thus, any
type of server environment, including various system architectures,
may employ various embodiments of the application-centric resources
system and method described herein and is considered as being
within a scope of the present invention.
[0013] For the purposes of this description the phrase "an on-line
social networking application" may be referred to as and used
interchangeably with the phrase "an on-line social network" or
merely "a social network." It will also be noted that an on-line
social network may be any type of an on-line social network, such
as, e.g., a professional network, an interest-based network, or any
on-line networking system that permits users to join as registered
members. For the purposes of this description, registered members
of an on-line social network may be referred to as simply
members.
[0014] Each member of an on-line social network is represented by a
member profile (also referred to as a profile of a member or simply
a profile). The profile information of a social network member may
include personal information such as, e.g., the name of the member,
current and previous geographic location of the member, current and
previous employment information of the member, information related
to education of the member, information about professional
accomplishments of the member, publications, patents, etc. The
profile information of a social network member may also include
information about the member's professional skills, such as, e.g.,
"product management," "patent prosecution," "image processing,"
etc.). The profile of a member may also include information about
the member's current and past employment, such as company
identifications, professional titles held by the associated member
at the respective companies, as well as the member's dates of
employment at those companies.
[0015] An on-line social network system also maintains information
about various companies, as well as so-called job postings. A job
posting, for the purposes of this description is an electronically
stored entity that includes information that an employer may post
with respect to a job opening. The information in a job posting may
include, e.g., the industry, job position, required and/or
desirable skills, geographic location of the job, the name of a
company, etc. The on-line social network system includes or is in
communication with a so-called recommendation system. A
recommendation system is configured to match member profiles with
job postings, so that those job postings that have been identified
as potentially being of interest to a member represented by a
particular member profile are presented to the member on a display
device for viewing. In one embodiment, the job postings that are
identified as of potential interest to a member are presented to
the member in order of relevance with respect to the associated
member profile.
[0016] Member profiles and job postings are represented in the
on-line social network system by feature vectors. The features in
the feature vectors may represent, e.g., a job industry, a
professional field, a job title, a company name, professional
seniority, geographic location, etc. A recommendation engine may be
provided in the form of a binary classifier that can be trained
using a set of training data. The set of training data can be
constructed using historical data that indicates whether a certain
job posting presented to a certain member resulted in that member
applying for that job. A trained binary classifier may be used to
generate, for a (member profile, job posting) pair, a value
indicative of the likelihood that a member represented by the
member profile applies for a job represented by the job posting. A
value indicative of the likelihood that a member represented by the
member profile applies for a job represented by the job posting may
be referred to as a relevance value or a degree of relevance. Those
job postings, for which their respective relevance values for a
particular member profile are equal to or greater than a
predetermined threshold value, are presented to that particular
member, e.g., on the news feed page of the member or on some other
page provided by the on-line social networking system. Job postings
presented to a member may be ordered based on their respective
relevance values, such that those job postings that are determined
to be more relevant (where the recommendation system determined
that the member is more likely to apply for jobs represented by
those listings as opposed to the jobs represented by other
postings) are presented in such a manner that they would be more
noticeable by the member, e.g. in a higher position in the list of
relevant job postings.
[0017] A recommendation engine that is provided in the form of a
binary classifier trains a binary classification model on the
(member profile, job posting) pairs and their corresponding labels
that indicate whether or not the member represented by the member
profile has applied for the job represented by the job posting. The
binary classification model would learn global weights that are
optimized to fit all the (member profile, job posting) pairs in the
data set. If the binary classification model treats each (member
profile, job posting) pair equally, the overall optimization result
may be biased towards those member profiles that have been paired
with a larger number of job postings as compared to those member
profiles that have been paired with a fewer number of job postings.
If the binary classification model treats equally each job posting
pared with a member profile, regardless, e.g., of whether the
associated member viewed the job posting or not, such that the
respective positions of job postings in the ranked list are
invisible in the learning process, the algorithm may unduly
emphasize unimportant or even irrelevant job postings (e.g., those
job postings that were ignored and not viewed by a respective
member). In the binary classification model, the degree of
relevance may not always be well modeled. For instance, it does not
take into consideration that even if a member does not apply for
certain jobs, a job posting that is impressed but not clicked by
the member may be inferred to be less relevant than the one that is
impressed and clicked by the same member.
[0018] A learning to rank approach may be utilized beneficially to
address some of these problems, as it takes into consideration
multiple ordered categories of relevance labels, such as, e. g.,
Perfect>Excellent>Good>Fair>Bad. A learning to rank
model can learn from pairwise preference (e.g., job posting A is
more relevant than job posting B for a particular member profile)
thus directly optimizing for the rank order of job postings for
each member profile. With ranking position taken into consideration
during training, top-ranked job postings may be treated by the
recommendation system as being of more importance than lower-ranked
job postings. In addition, a learning to rank approach may also
result in an equal optimization across all member profiles and help
minimize bias towards those member profiles that have been paired
with a larger number of job postings. In one example embodiment, a
recommendation system may be configured to produce relevance labels
mentioned above automatically without human intervention.
[0019] A recommendation system may be configured to generate
respective multi-point scale ranking labels for each (member
profile, job posting) pairs. The labels indicating different
degrees of relevance may be, e.g., in the format of Bad, Fair,
Good, Excellent, and Perfect. Using such label data, a
recommendation system may train a ranking model (also referred to
as a learning to rank model) that may be used by a ranker module of
the recommendation system to rank job postings for each member
profile, directly optimizing for the order of the ranking results
based on a metric such as, e.g., normalized discounted cumulative
gain (NDCG).
[0020] In one example embodiment, in order to train a learning to
rank model, the recommendation system constructs respective
five-point labels for (member profile, job posting) pairs,
utilizing feedback data collected by automatically monitoring
member interactions with job postings that have been presented to
them. In one embodiment, the relevance labels are defined as shown
below.
[0021] Bad Random: randomly generated synthetic (member profile,
job posting) pair of an active member profile with an active job
posting, where the job posting has not been presented to the
associated member, at all or for a certain period of time.
[0022] Fair Impressed: (member profile, job posting) pair, where
the job posting has been presented to the associated member
(impressed), but there has been no further interaction of the
associated member with the job positing, such as a click on the job
posting to view the details of the posting.
[0023] Good Clicked: (member profile, job posting) pair, where the
job posting has been presented to the associated member and the
recommendation system also detected a click on the job posting to
view the details of the posting, but no further event indicative of
applying for the associated job has been detected by the
recommendation system.
[0024] Excellent Applied: (member profile, job posting) pair, where
the job posting has been presented to the associated member, and
the recommendation system also detected that the member clicked on
the job posting to view the details and applied for the associated
job but did not detect a confirmation that the member has been
hired for that job.
[0025] Perfect Hired: (member profile, job posting) pair, where the
recommendation system detected a confirmation that the member has
been hired for that job. There are multiple ways to infer hired
event within our system, e.g. a) directly through recruiter
feedbacks, and b) through members' job change events, which can be
further interred from member updating certain fields of their
profile, such as changing of job location, job title and job
company.
[0026] It will be noted that although, in one embodiment, the
recommendation system uses five degrees of relevance, a
recommendation system may use a lesser or a greater number of
degrees, where each degree of relevance corresponds to a respective
temporal sequence of events, each one sequentially closer to the
final successful action of a member represented by a member profile
applying to a job represented by a job posting. A learning to rank
approach described herein may be utilized beneficially in other
settings, e.g., where each degree of relevance corresponds to a
respective geographic proximity to a given location.
[0027] In one example embodiment, a learning to rank model utilized
by a recommendation system uses boosted gradient decision trees
(BGDT) as the learning to rank algorithm. Once the recommendation
system generates multi-point scale relevance labels, it converts
these labels into numeric gains and uses the respective Discounted
Cumulative Gain (DCG) values as measurements and targets for the
model training. Table I below illustrates how different labels
correspond to respective relevance values (identified as "Grade" in
Table 1) and respective gains (identified as "Gain" in Table
1).
TABLE-US-00001 TABLE 1 Label Grade Gain Bad 0 0 Fair 1 1 Good 2 3
Excellent 3 7 Perfect 4 15
[0028] In Table 1, a Gain value is calculated as expressed in
Equation (1) below.
Gain=2.sup.Grade-1 Equation (1)
The Discounted Cumulative Gain (DCG) from position 1 to position p
in the list of results (e.g., in the list of references to
recommended job postings) can be defined as expressed below in
Equation (2).
DCG = i = 1 p Gain i log 2 ( i + 1 ) , where Gain i is the re
levance gain calculated for the item that appears in the list at
position i Equation ( 2 ) ##EQU00001##
[0029] NDCG can then be calculated as the DCG of the rank ordering,
divided by the DCG of the ideal ordering (as if returned by an
optimal ranker), which is expressed by Equation (3) below. NDCG is
always within range [0,1].
NDCG = DCG ranker DCG ideal Equation ( 3 ) ##EQU00002##
[0030] As mentioned above, the learning to rank algorithm may be in
the form of boosted gradient decision trees and can be directly
optimized for NDCG (as list-wise optimization). In Equation (3)
above, the DCGranker is calculated using the rank scores and
DCG.sub.ideal is calculated using the relevance labels. The error
for an intermediate ranker produced during the training process is
the difference between DCG_ranker and DCG_ideal, which can be used
in the tree training process with gradient decent. A small number
of small decision trees (e.g., decision trees with five leaves on
each tree) can be trained with boosting, where a relevance score
for a job posting with respect to a member profile is calculated as
the sum of tree scores calculated for that job posting with respect
to that member profile using respective decision trees, which is
illustrated in a diagram 300 shown in FIG. 3. A decision tree is
constructed to determine a ranking score calculated using
respective features or respective sets of features from a (member
profile, job posting) pair that is the subject of examination. For
example, one of the decision trees may be constructed to analyze
respective job title features from the member profile and the job
posting, and also to analyze the job company and location features
from the job posting. One of the decision nodes from the tree may
be to compare to a threshold value the cosine similarity matching
score calculated with respect to the job title feature (e.g.,
represented by a title string) in the member profile and the job
title feature from the job posting. Another decision node may be to
compare to a threshold value a popularity score indicative of how
popular is the company and its location represented by the job
company and job location features from the job posting. The
terminal nodes (leaf nodes) of a decision tree represent possible
outcomes of applying the decision tree to a (member profile, job
posting) pair. The outcomes are also referred to as tree
scores.
[0031] Example method and system to utilize learning to rank
modeling in the context of an on-line social network data may be
implemented in the context of a network environment 100 illustrated
in FIG. 1. As shown in FIG. 1, the network environment 100 may
include client systems 110 and 120 and a server system 140. The
client system 120 may be a mobile device, such as, e.g., a mobile
phone or a tablet. The server system 140, in one example
embodiment, may host an on-line social network system 142. As
explained above, each member of an on-line social network is
represented by a member profile that contains personal and
professional information about the member and that may be
associated with social links that indicate the member's connection
to other member profiles in the on-line social network. Member
profiles and related information may be stored in a database 150 as
member profiles 152. The database 150 may also store job postings
that may be viewed by members of the on-line social network system
142.
[0032] The client systems 110 and 12.0 may be capable of accessing
the server system 140 via a communications network 130, utilizing,
e.g., a browser application 112 executing on the client system 110,
or a mobile application executing on the client system 120. The
communications network 130 may be a public network (e.g., the
Internet, a mobile communication network, or any other network
capable of communicating digital data). As shown in FIG. 1, the
server system 140 also hosts a recommendation system 144. The
recommendation system 144 may be utilized beneficially to identify
and retrieve, from the database 150, the job postings that are
identified as of potential interest to a member represented by a
member profile. The recommendation system 144 identifies
potentially relevant job postings based on respective features that
represent the job postings and the member profile. These
potentially relevant job postings, which may be identified off-line
for each member or on-the-fly in response to a predetermined event
(e.g., an explicit request from a member), are presented to the
member in order of inferred relevance. The order of presentation
may be determined using a learning to rank model, as described
above and also further below with respect to FIGS. 2-5. A learning
to rank model may be trained using the training data stored in the
database 150 as training data 154. The training data may be
obtained automatically, as described above and also further below.
Example architecture 200 of a recommendation system is illustrated
in FIG. 2.
[0033] As shown in FIG. 2, the architecture 200 includes a
retrieval engine 210, a ranker 220, and a training data collector
230. The retrieval engine 210 retrieves a list of recommended jobs
240 from a database 250 for a particular member profile. The list
of recommended jobs 240 may be in a format {member ID
(job_posting_ID.sub.1, . . . , job_posting_ID.sub.n)}, where member
ID is a reference to a member profile and job posting items are
references to job postings that have been determined as being
potentially of interest to a member represented by the member
profile in the on-line social network system 142 of FIG. 1. The
ranker 220 executes a learning to rank model 222 with respect to
the list of recommended jobs 240 to generate a respective rank
score for each item in the list. The learning to rank model 222 may
use boosted gradient decision trees as a learning to rank
algorithm, where the terminal leaves in a decision tree represent
relevance scores that can be attributed to a job posting with
respect to a member profile. A rank score for an item in the list
is calculated as the sum of rank scores determined for each of the
decision trees, as shown in diagram 300 of FIG. 3. In FIG. 3, the
thicker edges show the decision tracks.
[0034] Returning to FIG. 2, the rank scores calculated by the
learning to rank model 222 are assigned to the items in the list of
recommended jobs 240. A list of recommended jobs with respective
assigned rank scores 260 is provided to the training data collector
230. The training data collector 230 monitors events with respect
to how the member, for whom the list of recommended jobs 240 was
generated, interacts with the associated job postings and, based on
the monitors interactions, assigns relevance labels to the items in
the list. As explained above, a job posting that is impressed and
clicked by the associated member receives a different relevance
score from a relevance label assigned to a job posting that was
impressed but not clicked by the associated member. A list of
recommended jobs with respective assigned relevance labels 270 is
provided to a repository of training data 280. The training data
stored in the database 280 is used to train the learning to rank
model 222. As explained above, the learning to rank model 222 can
be optimized for NDCG using the Equation (3) above, where
DCG.sub.ranker is calculated using the rank scores and
DCG.sub.ideal is calculated using the relevance labels.
[0035] An example recommendation system 144 of FIG. 1 is
illustrated in FIG. 4. FIG. 4 is a block diagram of a system 400 to
utilize learning to rank modeling in the context of an on-line
social network data, in accordance with one example embodiment. As
shown in FIG. 4, the system 400 includes a training data collector
410, a learning to rank module 420, a ranker 430, and a
presentation module 440. The training data collector 410 is
configured to collect training data. The training data, as
explained above, comprises a plurality of job lists (also referred
to as lists of recommended job postings). Each job list from the
plurality of job lists comprises respective identifications of a
plurality of job postings, each identification of a job posting
from the plurality of job postings is assigned a relevance label
indicating a grade of relevance of that job posting with respect to
a member profile associated with that job list. A job list may be
in the form as shown below.
TABLE-US-00002 {memberID JobPostingID.sub.1, label.sub.1), ...
JobPostingID.sub.n, label.sub.n)}
[0036] In operation, the training data collector 410, which
corresponds to the training data collector 230 of FIG. 2, accesses
a previously generated recommended jobs list associated with a
member profile, monitors and collects events data with respect to
interactions of the member represented by the member profile with
the recommended jobs list. Based on the events data, the training
data collector 410 assigns respective relevance labels to items in
the recommended jobs list. The recommended jobs list with the
assigned respective relevance labels is stored as training data,
e.g., in the database 280 of FIG. 2. In one embodiment, the
training data collector assigns a "Good" relevance label to an item
in the recommended jobs list responsive to detecting that the
member viewed a job posting represented by the item, an "Excellent"
relevance label responsive to detecting that the member applied to
a job posting represented by the item, and a "Bad" relevance label
responsive to detecting an absence of a viewing, by the member, of
a job posting represented by the item.
[0037] The learning to rank module 420 is configured to learn a
ranking model (a learning to rank model) using relevance labels
from the training data and rank scores calculated for (member
profile, job posting) pairs from the training data. The learning to
rank module 420 optimizes the ranking model for normalized
discounted cumulative gain value with respect to discounted
cumulative gain value calculated using the relevance labels from
the training data and discounted cumulative gain value calculated
using the rank scores. In one embodiment, the ranking model is in
the form of a plurality of decision trees, and a rank score for a
(member profile, job posting) pair from the training data is
calculated as the sum of relevance scores, each one of the
relevance scores calculated by executing a respective decision tree
from the plurality of decision trees utilizing feature values from
the (member profile, job posting) pair. A value from the feature
values from the (member profile, job posting) pair may be
indicative of a professional title, a geographic location, a
company name, etc.
[0038] The ranker 430 is configured to access a recommended jobs
list, which may be generated by the retrieval engine 210 of FIG. 2
and execute the ranking model to determine respective rank scores
for items in the recommended jobs list. The presentation module 440
causes the items from the recommended jobs list to be presented on
a display device in an order based on the determined respective
rank scores. Some operations performed by the system 400 may be
described with reference to FIG. 5.
[0039] FIG. 5 is a flow chart of a method 500 to utilize learning
to rank modeling in the context of an on-line social network data
to a social network member, according to one example embodiment.
The method 500 may be performed by processing logic that may
comprise hardware (e.g., dedicated logic, programmable logic,
microcode, etc.), software (such as run on a general purpose
computer system or a dedicated machine), or a combination of both.
In one example embodiment, the processing logic resides at the
server system 140 of FIG. 1 and, specifically, at the system 400
shown in FIG. 4.
[0040] As shown in FIG. 5, the method 500 commences at operation
510, when the training data collector 410 of FIG. 4 collects
training data, which is then stored in the database 280 of FIG. 2.
The training data is in the form of lists of respective
identifications of job postings, where each list is generated for a
particular member profile and where each jib posting identification
is associated with a relevance label, as shown below.
TABLE-US-00003 {memberID JobPostingID.sub.1, label.sub.1), ...
JobPostingID.sub.n, label.sub.n)}
[0041] At operation 520, the learning to rank module 420 of FIG. 4
learns a ranking model using the relevance labels from the training
data and the rank scores calculated for (member profile, job
posting) pairs from the training data, e.g., using boosted gradient
decision trees. At operation 530, the ranker 430 of FIG. 4 accesses
a recommended jobs list, which may be generated by the retrieval
engine 210 of FIG. 2 and executes the ranking model, at operation
540, to determine respective rank scores for items in the
recommended jobs list. The presentation module 440 causes the items
from the recommended jobs list to be presented on a display device
in an order based on the determined respective rank scores, at
operation 550.
[0042] 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.
[0043] 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.
[0044] FIG. 6 is a diagrammatic representation of a machine in the
example form of a computer system 600 within which a set of
instructions, for causing the machine to perform any one or more of
the methodologies discussed herein, may be executed. In alternative
embodiments, the machine operates as a stand-alone 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 a 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 a set of 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.
[0045] The example computer system 600 includes a processor 602
(e.g., a central processing unit (CPU), a graphics processing unit
(GPU) or both), a main memory 604 and a static memory 606, which
communicate with each other via a bus 606. The computer system 600
may further include a video display unit 610 (e.g., a liquid
crystal display (LCD) or a cathode ray tube (CRT)). The computer
system 600 also includes an alpha-numeric input device 612 (e.g., a
keyboard), a user interface (UI) navigation device 614 (e.g., a
cursor control device), a disk drive unit 616, a signal generation
device 618 (e.g., a speaker) and a network interface device
620.
[0046] The disk drive unit 616 includes a machine-readable medium
622 on which is stored one or more sets of instructions and data
structures (e.g., software 624) embodying or utilized by any one or
more of the methodologies or functions described herein. The
software 624 may also reside, completely or at least partially,
within the main memory 604 and/or within the processor 602 during
execution thereof by the computer system 600, with the main memory
604 and the processor 602 also constituting machine-readable
media.
[0047] The software 624 may further be transmitted or received over
a network 626 via the network interface device 620 utilizing any
one of a number of well-known transfer protocols (e.g., Hyper Text
Transfer Protocol (HTTP)).
[0048] While the machine-readable medium 622 is shown in an example
embodiment to be a single medium, the term "machine-readable
medium" should be taken to 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 sets of
instructions. The term "machine-readable medium" shall also be
taken to include any medium that is capable of storing and encoding
a set of instructions for execution by the machine and that cause
the machine to perform any one or more of the methodologies of
embodiments of the present invention, or that is capable of storing
and encoding data structures utilized by or associated with such a
set of instructions. The term "machine-readable medium" shall
accordingly be taken to include, but not be limited to, solid-state
memories, optical and magnetic media.
[0049] Such media may also include, without limitation, hard disks,
floppy disks, flash memory cards, digital video disks, random
access memory (RAMs), read only memory (ROMs), and the like.
[0050] The embodiments described herein may be implemented in an
operating environment comprising software installed on a computer,
in hardware, or in a combination of software and hardware. Such
embodiments of the inventive subject matter may be referred to
herein, individually 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.
Modules, Components and Logic
[0051] 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 (1) on a
non-transitory machine-readable medium or (2) in a transmission
signal) or hardware-implemented modules. A hardware-implemented
module is 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 processors may be
configured by software (e.g., an application or application
portion) as a hardware-implemented module that operates to perform
certain operations as described herein.
[0052] In various embodiments, a hardware-implemented module may be
implemented mechanically or electronically. For example, a
hardware-implemented 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-implemented 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-implemented 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.
[0053] Accordingly, the term "hardware-implemented module" should
be understood to encompass a tangible entity, be that an entity
that is physically constructed, permanently configured (e.g.,
hardwired) or temporarily or transitorily configured (e.g.,
programmed) to operate in a certain manner and/or to perform
certain operations described herein. Considering embodiments in
which hardware-implemented modules are temporarily configured
(e.g., programmed), each of the hardware-implemented modules need
not be configured or instantiated at any one instance in time. For
example, where the hardware-implemented modules comprise a
general-purpose processor configured using software, the
general-purpose processor may be configured as respective different
hardware-implemented modules at different times. Software may
accordingly configure a processor, for example, to constitute a
particular hardware-implemented module at one instance of time and
to constitute a different hardware-implemented module at a
different instance of time.
[0054] Hardware-implemented modules can provide information to, and
receive information from, other hardware-implemented modules.
Accordingly, the described hardware-implemented modules may be
regarded as being communicatively coupled. Where multiple of such
hardware-implemented modules exist contemporaneously,
communications may be achieved through signal transmission (e.g.,
over appropriate circuits and buses) that connect the
hardware-implemented modules. In embodiments in which multiple
hardware-implemented modules are configured or instantiated at
different times, communications between such hardware-implemented
modules may be achieved, for example, through the storage and
retrieval of information in memory structures to which the multiple
hardware-implemented modules have access. For example, one
hardware-implemented 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-implemented module may
then, at a later time, access the memory device to retrieve and
process the stored output. Hardware-implemented modules may also
initiate communications with input or output devices, and can
operate on a resource (e.g., a collection of information),
[0055] 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.
[0056] 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 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.
[0057] 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).)
[0058] Thus, learning to rank modeling in the context of an on-line
social network has been described. Although embodiments have 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 scope of the
inventive subject matter. Accordingly, the specification and
drawings are to be regarded in an illustrative rather than a
restrictive sense.
* * * * *