Patent Application
20150112765
The present application relates generally to data processing systems and, in one specific example, to techniques for identifying members of an online social network service that exhibit recruiting intent.
Online social and professional networking services are becoming increasingly popular, with many such services boasting millions of active members. In particular, the professional networking website LinkedIn has become successful at least in part because it allows members to actively recruit other members for jobs.
Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which:
Example methods and systems for identifying members of an online social network service that exhibit recruiting intent are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art that the present invention may be practiced without these specific details.
According to various exemplary embodiments, a recruiting intent determination system is configured to identify members that exhibit recruiting intent on a social network service such as LinkedIn. For example, the recruiting intent determination system may identify members of an online social network service that exhibit behavior indicating that they have an interest in recruiting and that they are actively using the online social network service for recruiting purposes.
For example, as illustrated in
As shown in
Once registered, a member may invite other members, or be invited by other members, to connect via the social network service. 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, are stored and maintained within the social graph, shown in
The social network service 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 social network service 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 social network service may host various job listings providing details of job openings with various organizations.
As members interact with the various applications, services and content made available via the social network service, the members' behavior (e.g., content viewed, links or member-interest buttons selected, etc.) may be monitored and information concerning the member's activities and behavior may be stored, for example, as indicated in
With some embodiments, the social network system 20 includes what is generally referred to herein as a recruiting intent determination system 300. The recruiting intent determination system 300 is described in more detail below in conjunction with
Although not shown, with some embodiments, the social network system 20 provides an application programming interface (API) module via which third-party applications can access various services and data provided by the social network service. 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 social network service that facilitates presentation of activity or content streams maintained and presented by the social network service. 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., phone, or tablet computing devices) having a mobile operating system.
Turning now to
As described in more detail below, the identification module 302 is configured to identify a set of members of an online social network service that self-identify as recruiters. The identification module 302 may then cluster the set of members that self-identify as recruiters into a first group of engaged recruiters and a second group of non-engaged recruiters. Moreover, the identification module 302 may categorize the group of engaged recruiters as members exhibiting recruiting intent.
Thereafter, the prediction module 304 is configured to access behavioral log data associated with the members exhibiting recruiting intent, and to classify the behavioral log data as recruiting intent signature data. Moreover, the prediction module 304 is configured to perform prediction modeling based on the recruiting intent signature data and a prediction model (e.g., a logistic regression model), in order to identify members of the online social network service that are associated with behavioral log data matching the recruiting intent signature data. Accordingly, the prediction module 304 may identify all members of an online social network service that exhibit recruiting intent at a given time. The operation of each of the aforementioned modules of the recruiting intent determination system 300 will now be described in greater detail in conjunction with
Referring back to the method 400 in
In some embodiments, the identification module 302 may identify these members by accessing member profile data of each of the members of the online social network service, and by identifying members that are associated with member profile data indicating that the member is a recruiter and/or self identifies a recruiter. For example, the identification module 302 may determine that a member is a recruiter based on any information in the member's profile data or on the member's profile page that indicates or suggests that the member is a recruiter.
For example, each member of an online social network service (e.g., LinkedIn) may be associated with a member profile page that includes various information about that member. An example of a member profile page 500 of a member (e.g., a LinkedIn® page of a member “Jane Doe”) is illustrated in
In some embodiments, by analyzing the member profile data and/or member profile page of a member of a social network service, the identification module 302 may identify various member attributes that indicate that the member is a recruiter. Examples of such recruiter attributes include a recruiter-focused experience position (e.g., the user Jane Doe in
Referring back to the method 400 in
As described herein, “clustering” is a process that involves separating or segmenting a group of members into one or more sub-groups or subsets of members, based on various clustering criteria. In some example embodiments, the clustering criteria may include measures of how engaged each of the recruiters are with various products of the online social network service, where the products may correspond to, for example, webpages, content within webpages, features, components, services, subscriptions, email/notification services, etc., associated with the online social network service. For example, in some embodiments, the identification module 302 may analyze the interactions between each of the recruiters 101 and the various products of the online social network, and the identification module 302 may then separate the recruiters 101 into the group of engaged recruiters 601 and the second group of non-engaged recruiters 602, based on the analyzed interactions. In other words, the identification module 302 may classify the recruiters 101 that demonstrate a high level of engagement with one or more products of the online social network service as engaged recruiters 601, and the identification module 302 may classify the recruiters 101 that demonstrate a relatively low level of engagement with one or more products of the online social network service as the non-engaged recruiters 602.
According to various exemplary embodiments, the identification module 302 may analyze the interactions between each of the recruiters and the various products of the online social network service, by first accessing behavioral log data describing various user actions, interactions, activity, behaviour, etc., associated with each of the recruiters. Such behavioral log data may take the form of records with information indicating that, for example, “user X viewed webpage W at time T”, or “user X clicked on portion P, feature F, user-interface element E, etc., on webpage W at time T”, and so on, as understood by those skilled in the art. Such behavioral log data may be stored at, for example, the database 32 illustrated in
After accessing the log data associated with each of the recruiters 101, the identification module 302 may determine how engaged each of the members of the online social network service are. For example, in some embodiments, the identification module 302 may analyze the number of page views of various webpages of the social network service (e.g., homepages, jobs-related webpages, career-related webpages, recruiting-related webpages, advertising-related webpages, member profile webpages, group profile webpages, company profile webpages, education profile webpages, influencer profile webpages, news/updates webpages, mail/inbox webpages, etc.) by each of the recruiters in a given time period, in order to determine a level of engagement that each of the recruiters has with the online social network service. For example, recruiters that regularly (and/or have recently) viewed a significant number of one or more of the aforementioned webpages may be classified by the identification module 302 as engaged recruiters 601, whereas recruiters that have not regularly (and/or have not recently) viewed a significant number of one or more of the aforementioned webpages may be classified by the identification module 302 as engaged recruiters 602. In this way, the identification module 302 may separate the recruiters 101 into a group of engaged recruiters 601 and a group of non-engaged recruiters 602.
In some embodiments, the identification module 302 may take into account the total number of page views of multiple webpages or multiple different types of webpages, whereas in other embodiments, the identification module 302 may take into account the total number of page views of a particular webpage or a particular type of webpage. In some embodiments, views of a specific type or types of webpages (such as a recruiting-focused webpages or jobs webpage) may be ranked higher by the identification module 302 than the views of other types of webpages (such as a company webpage, influencer webpage, University webpage, etc.).
In some embodiments, the identification module 302 may calculate an engagement score associated with each of the recruiters 101 representing a level of engagement of each of the recruiters 101 with various products of the online social network service (using one or more techniques described above). For example, recruiters that regularly (and/or have recently) viewed a significant number of webpages associated with the social network service may be assigned a higher engagement score by the identification module 302, whereas recruiters that have not regularly (and/or have not recently) viewed a significant number of one or more of webpages associated with the online social network service may be assigned a lower engagement score by the identification module 302. Thereafter, the identification module 302 may separate the recruiters 101 into a group of engaged recruiters 601 and a group of non-engaged recruiters 602, based on the engagement scores associated with each of the recruiter's 101. For example, in some embodiments, the identification module 302 may categorize any recruiter 101 with an engagement scores greater than a predetermined threshold as an engaged recruiters 601, and categorize any recruiter 101 with an engagement score lower than a predetermined threshold as a non-engaged recruiter 602. In other embodiments, the identification module 302 may determine an average, median, or mean engagement score for all the recruiters 101, and any one of the recruiters 101 having a greater engagement score may be categorized as an engaged recruiter 601, whereas any recruiter 101 with a lower engagement score may be categorized as a non-engaged recruiter 602. The identification module 302 may use any other statistical analysis techniques understood by those skilled in the art in order to cluster the recruiters 101 (e.g., the identification module 302 may analyze the distribution of engagement scores of each of the recruiters 101, in order to identify statistically-significant clusters of higher engagement scores and lower engagement scores, etc.).
Various examples above refer to an analysis of page views of various webpages in order to determine level of engagement of each of the recruiters 101 with various products of the online social network service. However, it is understood that the identification module 302 may analyze any aspect of user actions, interactions, activity, behaviour, etc., associated with each of the recruiters 101, in order to determine a level of engagement of each of the recruiters 101 with the various products of the online social network service. For example, the identification module 302 may analyze a number of times a recruiter has transmitted or received a notification message (e.g., a LinkedIn career-mail message), a number of times a recruiter has accessed a jobs page, a number of times a recruiter has posted a job on a jobs page, a number of times recruiter has viewed a job posted on a jobs page, number of times a recruiter has submitted various types of social activity signals (e.g., likes, shares, follows, comments, views, hover responses, close/hide responses, conversions, etc.) in association with various types of content posted on an online social network service, and so on.
According to various exemplary embodiments, the identification module 302 may validate the clustering of the recruiters 101 into the group of engaged recruiters 601 and the group of non-engaged recruiters 602, by determining that an engagement metric associated with the group of engaged recruiters 601 indicates a greater degree of engagement with the online social network service in comparison to the same engagement metric for the group of non-engaged recruiters 602. For example, the aforementioned engagement metric may include a measure of a number of days of active use of the online social network service during a specific time period. In some embodiments, if the identification module 302 determines that the a particular one of the engaged recruiters 601 is associated with a lower engagement metric (e.g., a low number of days of active use of the online social network service during a specific time period), then the identification module 302 may reassign this particular member to the group of non-engaged recruiters 602. It is understood that the engagement metric of ‘days of active use’ is simply one non-limiting example of an engagement metric that may be utilized during this validation process, and other engagement metrics understood by those skilled in the art may be used during this validation process. This validation process may occur after, for example, the operation 402 in the method 400.
Referring back to the method 400 in
According to various exemplary embodiments, the identification module 302 may validate the categorization of the engaged recruiters 601 as members exhibiting recruiting intent 601a. In other words, the identification module 302 may check that each of the engaged recruiters 601 are actually members exhibiting recruiting intent, because it is possible that some of the engaged recruiters 601 may not be actively recruiting. For example, perhaps one of the engaged recruiters 601 self-identified themselves as a recruiter on their profile by mistake, or perhaps one of the engaged recruiters 601 previously self-identified themselves as a recruiter on their profile, but they are no longer actively recruiting and they have not updated their profile.
According to various exemplary embodiments, the identification module 302 may validate the categorization of the engaged recruiters 601 as members exhibiting recruiting intent 601a, by determining that likely indicators of recruiting intent are overrepresented in the group of engaged recruiters 601. This validation process may occur after, for example, the operation 403 in the method 400. According to various exemplary embodiments, likely indicators of recruiting intent may include a number of jobs posted by a member, a number of career mail messages transmitted by a member, and a member subscription to a talent-finder service, and so on. Note that, as described herein, a career mail message is an email message (or some other type of electronic message/notification) where the sender explicitly specifies the category of the email message as “Career opportunity” or something related to recruiting (e.g., by selecting the category of “Career opportunity” from a list of email category options). Such career mail messages may be transmitted via a messaging service associated with an online social network service such as LinkedIn. Accordingly, in some example embodiments, the identification module 302 identifies a career mail message simply by analyzing the relevant category information associated with the email (e.g., category information included in the header of the email), and thus the identification module 302 does not parse the actual subject or message contents of the email composed by the sender when determining if the email is a career mail message.
Accordingly, in various exemplary embodiments, the identification module 302 may access behavioral log data associated with each of the engaged recruiters 601, and may check that the likely indicators of recruiting intent are overrepresented in this behavioral log data. In some embodiments, data describing the aforementioned likely indicators of recruiting intent may be stored locally at, for example, the database 306 illustrated in
In some exemplary embodiments, if the identification module 302 determines that the behavioral log data associated with a particular member of the engaged recruiters 601 does not include the aforementioned indicators of recruiting intent, the identification module 302 may reclassify this member as belonging to the group of non-engaged recruiters 602. However, it is understood that this technique is optional. For example, in other embodiments, after the clustering is performed, the system will not reassign members who are not positive for one or more of the likely indicators of recruiting intent. This is because such members have nevertheless exhibited other signals that make up the overall signature that caused them to be clustered with the highly engaged group 601.
According to various exemplary embodiments, the clustering criteria used for the clustering process (in operation 402 in the method 400) may be different from the likely indicators of recruiting intent that are used to verify the clustering process. For example, if the identification module 302 clusters the recruiters 101 based in part on a number of jobs posted by a member, then this specific behavioral signal will not be utilized by the recruiting intent determination system 300 for the purposes of validating the clustering of the recruiters 101. Similarly, if the indicators of recruiting intent that will be utilized for the purposes of validating the clustering include a number of jobs posted by a member, a number of career mail messages transmitted by a member, and a member subscription to a talent-finder service, then these signals are not utilized by the recruiting intent determination system 300 during the clustering process itself. This technique may be advantageous because the validation of the clustering is more effective if it is performed based on different behavioral signals then those used in the clustering process itself.
Referring back to the method 400 in
In operation 405 in
The prediction module 304 may use any one of various known prediction modeling techniques to perform the prediction modeling. For example, according to various exemplary embodiments, the prediction module 304 may apply a statistics-based machine learning model such as a logistic regression model to the recruiting intent signature data. 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. A highly simplified example machine learning model using logistic regression may be ln[p/(1−p)]=a+BX+e, or [p/(1−p)]=exp(a+BX+e), where ln is the natural logarithm, logexp, where exp=2.71828 . . . , p is the probability that the event Y occurs, p(Y=1), p/(1−p) is the “odds ratio”, ln[p/(1−p)] is the log odds ratio, or “logit”, a is the coefficient on the constant term, B is the regression coefficient(s) on the independent/predictor variable(s), X is the independent/predictor variable(s), and e is the error term. In some embodiments, the independent/predictor variables of the logistic regression model may be behavioral log data associated with members of an online social network service (where the behavioral log data may be encoded into feature vectors). The regression coefficients may be estimated using maximum likelihood or learned through a supervised learning technique from the recruiting intent signature data, as described in more detail below. Accordingly, once the appropriate regression coefficients (e.g., B) are determined, the features included in a feature vector (e.g., behavioral log data associated with a member of a social network service) may be plugged in to the logistic regression model in order to predict the probability that the event Y occurs (where the event Y may be, for example, a particular member of an online social network service having recruiting intent). In other words, provided a feature vector including various behavioral features associated with a particular member, the feature vector may be applied to a logistic regression model to determine the probability that the particular member has recruiting intent. 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. The prediction module 304 may use various other prediction modeling techniques understood by those skilled in the art to predict whether a particular has recruiting intent. For example, other prediction modeling techniques may include other machine learning models such as a Naïve 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.
According to various embodiments described above, the recruiting intent signature data may be used for the purposes of both off-line training (for generating, training, and refining a prediction model and or the coefficients of a prediction model) and online inferences (for predicting whether a particular member exhibits recruiting intent). For example, if the prediction module 304 is utilizing a logistic regression model (as described above), then the regression coefficients of the logistic regression model may be learned through a supervised learning technique from the recruiting intent signature data. Accordingly, in one embodiment, the recruiting intent determination system 300 may operate in an off-line training mode by assembling the recruiting intent signature data into feature vectors. (For the purposes of training the system, the system generally needs both positive examples of behaviour of members having recruiting intent, as well as negative examples of behaviour of members that do not have recruiting intent, as will be described in more detail below). The feature vectors may then be passed to the prediction module 304, in order to refine regression coefficients for the logistic regression model. For example, statistical learning based on the Alternating Direction Method of Multipliers technique may be utilized for this task. Thereafter, once the regression coefficients are determined, the recruiting intent determination system 300 may operate to perform online (or offline) inferences based on the trained model (including the trained model coefficients) on a feature vector representing the behaviour of a particular member of the online social network service. For example, according to various exemplary embodiments described herein, the recruiting intent determination system 300 is configured to predict the likelihood that a particular member has recruiting intent, based on whether the behaviour of the particular member matches or conforms to the recruiting intent signature data that was utilized to train the model. In some embodiments, if the probability that the particular member has recruiting intent is greater than a specific threshold (e.g., 0.5, 0.8, etc.), then the prediction module 304 may classify that particular member as having recruiting intent. In other embodiments, the prediction module 304 may calculate a recruiting intent score for the particular member, based on the probability that the particular member has recruiting intent. Accordingly, the prediction module 304 may repeat this process for all the members of an online social network service.
According to various exemplary embodiments, the off-line process of training the prediction model based on the recruiting intent signature data may be performed periodically at regular time intervals (e.g., once a day), or may be performed at irregular time intervals, random time intervals, continuously, etc. Thus, since recruiting intent signature data may change over time based on changes in the behavior of the members exhibiting recruiting intent 601a, it is understood that the prediction model itself may change over time (based on the current recruiting intent signature data being used to train the model). The behaviour of people having recruiting intent 601a may change over time because, for example, industry practice within the field of recruiting may change, or features, products and technology of the online social network service may change, and so on. Thus, the operation 405 in the method 400 may comprise identifying all the members of an online social network service that are exhibiting recruiting intent at a specific time.
Non-limiting examples of behaviour representative of members having recruiting intent (e.g., the positive examples described above) may include transmitting or receiving a particular number of mail messages (e.g., career mail messages), posting a particular number of jobs, viewing a particular number of jobs, viewing a particular amount of member profiles, performing a particular number of searches for members, and so on. Non-limiting examples of behavior representative of members not having recruiting intent (e.g., the negative examples described above) includes a particular number of views of jobs-related pages (e.g., jobs detail pages), a particular number of views of jobs seeking home pages, a particular number of views of a member's own profile, and a particular number of company searches, and so on. Of course, such behavioral signals are merely exemplary, and the behavioral signals identified by the prediction module 304 may change continuously as the ecosystem of the online social network service evolves over time.
As described above, for the purposes of training the logistic regression prediction model, the prediction model generally requires both positive examples of behaviour of members having recruiting intent, as well as negative examples of behaviour of members that do not have recruiting intent. According to various exemplary embodiments, the aforementioned recruiting intent signature data (i.e., behavioral log data associated with the engaged recruiters 601) may be classified as positive examples for training the prediction model. In other words, the recruiting intent signature data may be treated by the prediction module 304 as representative samples of behavior associated with members having recruiting intent, and the prediction module 304 may train the prediction model based on the recruiting intent signature data (e.g., by refining the coefficients of the prediction model). In this way, the prediction model may be later utilized to analyze behavioral log data associated with a given member, in order to determine whether such behavioral log data conforms to or matches the positive samples (i.e. the recruiting intent signature data), and to thus determine whether the given member has recruiting intent. For example, as illustrated in
According to various exemplary embodiments, behavior log data associated with the group of non-engaged recruiters 602 may be classified as negative training samples for training the prediction model. Moreover, behavior signal data associated with a random selection of members of the online social network service that (1) do not self-identify as recruiters and that (2) do not exhibit the likely indicators of recruiting intent described above, may also be classified as negative training samples for training the prediction model. In other words, the aforementioned data may be treated by the prediction module 304 as representative samples of behavior associated with members that do not have recruiting intent, and the prediction module 304 may train the prediction model based on the such data (e.g., by refining the coefficients of the prediction model). In this way, the prediction model may be later utilized to analyze behavioral log data associated with a given member, in order to determine whether such behavioral log data conforms to or matches the negative samples, and to thus determine whether the given member does not have recruiting intent. For example, as illustrated in
According to various exemplary embodiments, the prediction module 304 is configured to assign a recruiting intent score to each of the members of the online social network service. Based on the recruiting intent score, the prediction module 304 may determine whether each member of the online social network service is a member exhibiting recruiting intent or not. The recruiting intent determination system 300 may then adjust a content experience of each member of the online social network service, depending on whether that member exhibits recruiting intent or not.
For example,
In operation 1003 in
According to various exemplary embodiments, after determining that a particular member exhibits recruiting intent, the prediction module 304 may also adjust a content experience on the online social network service for other members that may interact with this particular member exhibiting recruiting intent. For example, in some embodiments, when another member of the online social network service views the profile page of this particular member, the prediction module 304 may display recruiter badge information on the member profile page of this particular member indicating that this particular member is a recruiter, is currently recruiting, is currently looking for talent, etc. In some embodiments, the prediction module 304 may display this recruiter badge information to any members of the online social network service that view the member profile page of this particular member. In other embodiments, the prediction module 304 may selectively display this recruiter badge information only to members of the online social network service designated by the system 300 as job seekers. For example, in some embodiments, the system 300 may include a job seeker prediction module 308 (illustrated in
According to various exemplary embodiments, the job seeker prediction module 308 may determine that a member is a job seeker based on, for example, whether the member looks at a particular number of job postings during a given time interval, or whether the member signs up for a job seeker subscription on LinkedIn, or whether the member views articles related to finding jobs, and so on. Various techniques for identifying job seekers (e.g., by the job seeker prediction module 308 of the system 300) are described in greater detail in pending U.S. patent application Ser. No. 13/684,013, filed on Nov. 21, 2012, entitled “Customizing a user experience based on a job seeker score”, which is incorporated by reference herein.
In some embodiments, the prediction module 304 may display a list of recruiters to the job seekers identified by the job seeker prediction module 308, where the list of recruiters may be ranked based on the recruiting intent score associated with each of the recruiters (e.g., the recruiter with the highest recruiting intent score is displayed highest in the list).
In some embodiments, if the aforementioned job seeker prediction module 308 determines that a particular user is a job seeker, then the job seeker prediction module 308 may determine whether this job seeker is a good candidate for a job posted by a members exhibiting recruiting intent (or posted by a company or university associated with a member exhibiting recruiting intent) by, for example, comparing attributes of the job seeker with job requirements criteria associated with the posted job. The prediction module 304 may then recommend such a job seeker as a job candidate to the member exhibiting recruiting intent. Thus, if the system 300 determines that a member is a job seeker and is an excellent match for a job posted by a company, then the system 200 may transmit a message to a member of the company that exhibits recruiting intent (e.g., a manager, CXO, etc.), where the message recommends the job seeker as a job candidate for the posted job.
According to various exemplary embodiments, the aforementioned job seeker prediction module 308 may determine that a job seeker is searching for jobs associated with a particular industry (e.g., software programming), a particular location (e.g., the San Francisco Bay Area), a particular company (e.g., Google), particular skills (e.g., software engineering), particular experience or education credentials (e.g., B.S.E. in software engineering), and so on, by analyzing the jobs being viewed by the job seeker. Thereafter, the prediction module 304 may display a list of members with matching attributes (e.g., matching industry, matching location, matching company, matching skills, matching experience, matching education credentials, etc.), where the list of members is ranked based on their recruiting intent score. Accordingly, if the recruiting intent determination system 300 determines that a user is attempting to find a job at Google, for example, then the prediction module 304 may display the list of employees that work at Google that have the highest recruiting intent scores. The prediction module 304 may then invite the job seeker to connect with this individual, to transmit a message to this individual, and so on.
According to various exemplary embodiments, if the prediction module 304 determines that a particular member does not exhibit recruiting intent, the prediction module 304 may adjust content experience of this member accordingly, such as by directing them away from recruiter-focused content such as recruiter subscription packages. For example,
Various embodiments throughout describe a system configured to identify members of an online social network service that are currently using the service for the purposes of recruiting. According to various exemplary embodiments, the various techniques and embodiments described herein may instead or in addition be applied for identifying members actively using an online social network service for other efforts (e.g., sales, marketing, advertising, job searching, etc.). For example, in some embodiments, the system may identify members that self-identify as salespeople, marketers, advertisers, and so on, and thereafter the system may cluster these members into engaged and non-engaged members (e.g., engaged and non-engaged salespeople, marketers, advertisers, etc.), consistent with various embodiments described above. Thereafter, the system may access signature behavioral log data associated with the engaged members, and perform a prediction modeling process based on this behavioral log data in order to ultimately identify all members that exhibit behavior matching the aforementioned signature behavioral log data, consistent with various embodiments described herein. Accordingly, the system may identify all members of the online social network service that have sales intent, marketing intent, advertising intent, etc. (e.g., all the members that are currently actively using the online social network service for sales, marketing, advertising, and so on). Thereafter, the system may just a content experience for each of these members. For example, the members that have sales intent may be provided with recommendations for sales-related content, subscription offers, articles, publications, advertisements, news items, member connection recommendations, group connection recommendations, and so on.
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 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 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.
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.
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.
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).
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.
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.
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).)
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.
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.
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 field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).
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.
The example computer system 1200 includes a processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1204 and a static memory 1206, which communicate with each other via a bus 1208. The computer system 1200 may further include a video display unit 1210 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 1200 also includes an alphanumeric input device 1212 (e.g., a keyboard or a touch-sensitive display screen), a user interface (UI) navigation device 1214 (e.g., a mouse), a disk drive unit 1216, a signal generation device 1218 (e.g., a speaker) and a network interface device 1220.
The disk drive unit 1216 includes a machine-readable medium 1222 on which is stored one or more sets of instructions and data structures (e.g., software) 1224 embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 1224 may also reside, completely or at least partially, within the main memory 1204 and/or within the processor 1202 during execution thereof by the computer system 1200, the main memory 1204 and the processor 1202 also constituting machine-readable media.
While the machine-readable medium 1222 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 invention, 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.
The instructions 1224 may further be transmitted or received over a communications network 1226 using a transmission medium. The instructions 1224 may be transmitted using the network interface device 1220 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, LTE, 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.
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 invention. 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.
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.
