Abstract: Techniques for predicting relevance of social networking service member accounts to a job posting. In an embodiment, a candidate predictor engine of a system encodes data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting. Additionally, the system stores the encoded data and assigns member-level weights to each of the applicants. Moreover, the system calculates weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores and member-level weights. Furthermore, the system sums the weighted AQ scores to derive a total weighted score for the job posting. Then, the candidate predictor engine generates a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the posting being filled by an applicant. Also, the system transmits the job-level pCH to a client for display.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Key Feature Engine receives a request for a desired number of key features to be identified from a data set in at least a portion of a database. The Key Feature Engine executes instances of multiple types of machine learning data models on the data set to calculate respective regression coefficients, in each machine learning data model instance, for each feature in a plurality of features defined by a plurality of data categories of a social network service. The Key Feature Engine identifies at least one key feature, of one or more instances of the multiple types of machine learning data models, based on a value of a corresponding regression coefficient.

Abstract: Techniques for searching for generating and using an online activity index for users of an online service are disclosed herein. In some example embodiments, for each one of a plurality of dimensions of online activity for an online service, the system determines a corresponding value for each one of a set of one or more activities corresponding to the dimension for a user of the online service, with the value representing a level of engagement by the user in the corresponding activity via the online service. The system generates a corresponding activity index for each one of the dimensions based on the corresponding value for each one of the set of one or more activities corresponding to the dimension. The system generates content based on the corresponding activity index of at least one of the dimensions, and causes the content to be displayed on a device associated with the user.

Abstract: Techniques for predicting relevance of social networking service member accounts to a job posting. In an embodiment, a candidate predictor engine of a system encodes data representing an applicant quality (AQ) score for each job/applicant pair for a plurality of applicants to a job posting. Additionally, the system stores the encoded data and assigns member-level weights to each of the applicants. Moreover, the system calculates weighted AQ scores for each of the job/applicant pairs, the weighted AQ scores being products of respective AQ scores and member-level weights. Furthermore, the system sums the weighted AQ scores to derive a total weighted score for the job posting. Then, the candidate predictor engine generates a job-level probability of confirmed hire (pCH) based on the total weighted score, the job-level pCH indicating a likelihood of the posting being filled by an applicant. Also, the system transmits the job-level pCH to a client for display.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Key Feature Engine receives a request for a desired number of key features to be identified from a data set in at least a portion of a database. The Key Feature Engine executes instances of multiple types of machine learning data models on the data set to calculate respective regression coefficients, in each machine learning data model instance, for each feature in a plurality of features defined by a plurality of data categories of a social network service. The Key Feature Engine identifies at least one key feature, of one or more instances of the multiple types of machine learning data models, based on a value of a corresponding regression coefficient.

Abstract: Techniques for searching for generating and using an online activity index for users of an online service are disclosed herein. In some example embodiments, for each one of a plurality of dimensions of online activity for an online service, the system determines a corresponding value for each one of a set of one or more activities corresponding to the dimension for a user of the online service, with the value representing a level of engagement by the user in the corresponding activity via the online service. The system generates a corresponding activity index for each one of the dimensions based on the corresponding value for each one of the set of one or more activities corresponding to the dimension. The system generates content based on the corresponding activity index of at least one of the dimensions, and causes the content to be displayed on a device associated with the user.

Abstract: Techniques for publishing a profile timeline of a member are described.. publisher receives, from a device of a viewer, a request to view a profile page of a member of an online social network. The publisher accesses employment data of the member having a first job associated with a first job title at a first employer with a specific duration. Additionally, the publisher can select a plurality of other members, each member in the plurality of other members having a similar job title related to the first job title. Furthermore, the publisher can determine an average duration for the first job title based on information accessed from the plurality of other members, and generate a first timeframe using the accessed employment data of the member. Subsequently, the publisher causes a presentation, on a display of the device, of the profile page of the member having the generated first timeframe.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for Candidate Predictor Engine (“C.P. Engine”) instantiates data structure(s) modeled according to a plurality of job candidate predictor decision trees. The C.P. Engine encodes data representing a job candidate context feature set based on an attribute(s) of a target candidate account and an attribute(s) of a job posting. The C.P. Engine filters each respective feature from the job candidate context feature set through a job candidate predictor decision tree that corresponds to a same job candidate context feature type as the respective feature. The C.P. Engine generates prediction output based on each result of filtering through the job candidate predictor decision trees. The prediction output indicates a likelihood of the target candidate account being qualified for the job posting.

Abstract: Estimation of workforce skill gaps using social network services are described herein. An unfilled job is represented by a job posting on a social network service. A skill is predicted as being required for the unfilled job by determining that each member of a set of members has an electronic profile on the social network service listing the skill as possessed by the member. A quantity of unfilled jobs on the social network service requiring the predicted skill is calculated. A quantity of selected job-seeking members of the social network service is calculated, each selected job-seeking member having an electronic profile on the social network service listing the predicted skill as possessed by the selected job-seeking member. A workforce skill gap for the predicted skill is estimated by subtracting the calculated quantity of job-seeking members from the calculated quantity of unfilled jobs.

Abstract: Techniques for upselling a limited job posting to a premium job posting are described. A determination module can access job listing data from a limited job. Additionally, the determination module can access member data from a social network. Furthermore, the determination module can determine a value for the limited job posting based on the accessed job listing data and the accessed member data. Moreover, the determination module can generate a job application based on the accessed job listing data and the accessed member data, when the determined value is above a predetermined threshold. Subsequently, the determination module and an upsell module can upsell the limited job posting to a premium job posting by using the generated job application data. In some instances, the upsell module can market to the job poster in order to upsell the limited job listing, and fill empty job slots already paid by the job poster.

Abstract: Systems and methods are presented for recommending volunteer opportunities to users in an online network. In some embodiments, a method is presented. The method may include accessing, in a device, at least one member profile attribute of a user in an online network, the at least one member profile attribute associated with a plurality of volunteer opportunities. The method may also include accessing information associated with the plurality of volunteer opportunities, generating a relevance score for each of the plurality of volunteer opportunities based on the at least one member profile attribute and the information associated with the plurality of volunteer opportunities, ranking the plurality of volunteer opportunities based on the relevance scores, and displaying at least some of the plurality of volunteer opportunities to the user based on the ranking.

Abstract: Techniques for determining that a member viewing a job posting has hiring authority are described. A determination module can identify a member viewing a job posting and access member data for the identified member. Additionally, the determination module can determine an entity associated with the job posting based on the accessed member data, and access job listing data for the determined entity. Moreover, the determination module can determine a score value based on the accessed member data and the accessed job listing data. The score value corresponds to a likelihood that the identified member has hiring authority for the job posting. Furthermore, an upsell module can upsell the job listing to the identified member based on the score value being higher than a predetermined threshold.

Abstract: Techniques for measuring the value of marketing contributions to deals are discussed. An account interest score of an account for a product or service is generated at a time of sale of the product or service to the account. The generating may be based on the individual interest scores of the members of the account for the product or service. The individual interest scores may be calculated based on marketing output that pertains to the product or service and is received by the members of the account before the sale. A determination is made that the account interest score exceeds a marketing contribution threshold value. Based on the determination that the account interest score exceeds the marketing contribution threshold value, a credit for the sale is assigned to a marketing organization that created the marketing output received by the members of the account before the sale.

Abstract: Techniques for determining the likelihood of a sales lead to purchase a product or service based on an interest score of a company account generated using individual interest scores of the members of the company account are described. For example, a first individual interest score of a first user for a product or service and a second individual interest score of a second user for the product or service are received. Using account data that identifies members of a company account, a determination is made that the first user and the second user are members of the same company account. An account interest score of the company account for the product or service is generated, using at least one computer processor, based on combining the first individual interest score and the second individual interest score.