Abstract: In an example embodiment, a machine learned model that integrates a generalized linear mixed model (GLMix) non-linear optimization is utilized to jointly perform personalized communications targeting and volume control. The machine learned model may be trained to not only maximize user engagement with a notification generally, such as maximizing the total number of people who view, save, or apply for a job associated with a job listing in the communication, but also trained to maximize an end goal of the notification, such as the total number of people who apply for the job associated with a job listing in the communication.

Abstract: In an example embodiment, a machine learned model that integrates a generalized linear mixed model (GLMix) non-linear optimization is utilized to jointly perform personalized communications targeting and volume control. The machine learned model may be trained to not only maximize user engagement with a notification generally, such as maximizing the total number of people who view, save, or apply for a job associated with a job listing in the communication, but also trained to maximize an end goal of the notification, such as the total number of people who apply for the job associated with a job listing in the communication.

Abstract: In an example embodiment, a fast and space-efficient ranking mechanism is used for vertical searches. A precedence is assigned to a plurality of features based on the relative importance of those features to the provider of a vertical search engine. In an example embodiment, this relative importance may be explicitly indicated by the provider of the vertical search engine. A unique data structure may then be created that incorporates the precedence of each feature.

Abstract: In an example embodiment, a fast and space-efficient ranking mechanism is used for vertical searches. A precedence is assigned to a plurality of features based on the relative importance of those features to the provider of a vertical search engine. In an example embodiment, this relative importance may be explicitly indicated by the provider of the vertical search engine. A unique data structure may then be created that incorporates the precedence of each feature.

Abstract: Techniques for enhancing usability and electronic resource efficiency using job relevance are disclosed herein. In some embodiments, a plurality of members of the social networking system are identified for which the new job is relevant. The job posting is identified as a top job posting for each of a subset of the plurality of members for which the new job is relevant, the identifying of the job posting as a top job posting including accessing an online data store that includes enhanced relevancy data, the enhanced relevancy data including real-time feedback provided by one or more of the plurality of members with respect to the values of the attributes specified within the job posting. A specialized notification of the top job posting is generated for each of the identified subset of the identified plurality of members.

Abstract: Techniques for presenting a personalized job posting are described. A job publisher can access member data of a first member from a member database and a plurality of job postings from a job database. A job score for each job posting in the plurality of job postings can be calculated based on the profile data of the first member and the job data for each job posting. Additionally, a relevant job posting for the first member can be determined based on the calculated job score for each job posting in the plurality of job postings. Moreover, a second member for the relevant job posting can be selected based the social graph data of the first member and the job data for the relevant job posting. Furthermore, a presentation of the relevant job posting can be on a display of a device of the first member.

Abstract: A system and method for determining similar members in an organization is disclosed. In some example embodiments, the social networking system receives a request for a particular job listing from a client system associated with a first member of a social networking system. The social networking system determines a first employment role for a job associated with the particular job listing. The social networking system identifies a source organization for the particular job listing. The social networking system identifies one or more similar members, wherein each identified similar member is associated with the source organization and has an employment role similar to the first employment role. The social networking system communicates the particular job listing and the identified one or more similar members to the client system for display.

Abstract: System and methods for generating recommendations based on a determined skill gap are disclosed. A social networking system determines an employment role associated with a particular job listing, wherein the particular job listing has an associated source organization. The social networking system identifies one or more similar members associated with the source organization and having an employment role similar to the determined employment role. The social networking system generates a composite list of skills associated with the one or more similar members. The social networking system compares the skills included in the composite list of skills with a list of skills associated with the particular job listing to determine a list of missing skills.

Abstract: A system and method for determining likely co-workers for a particular job listing is disclosed. The social networking system receives a request for a particular job listing from a client system associated with a first member of a social networking system. The social networking system determines one or more likely co-workers for a job described in the particular job listing. The social networking system communicates the particular job listing and member information for the determined one or more likely co-workers to the client system for display.