Abstract: Disclosed herein are techniques for generating contextual follow recommendations. Consistent with embodiments of the present invention, for each of several specific contexts—for example, a member opts to follow another specific member—a set of contextual follow recommendations are pre-computed. Then, in real time, when follow recommendations are being presented to the member, the recommendation system will first make a determination as to whether a member has taken action consistent with any particular context, and if so, a set of pre-computed contextual follow recommendations will be retrieved for possible presentation to the member.

Abstract: Described herein is a technique to generate and present follow recommendations. During a first stage or phase, training data are obtained by presenting follow recommendations to some randomly selected set of members, and then observing the collective members' responses. Using the training data, first and second predictive machine-learned scoring models are derived—the first scoring model for use in predicting when a member will opt to follow an entity being recommended, and the second scoring model for use in predicting if the member will engage with content presented via a newly formed follow edge. Then, using the scoring models, follow recommendations are derived, scored, and ultimately selected—based on their scores—for presentation to a member.

Abstract: In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.

Abstract: Methods, systems, and computer programs are presented for providing a user experience that facilitates navigation among different topics and articles on a social network. One method includes an operation for identifying a hierarchy of topics, each topic corresponding to a respective subject, where the hierarchy defines relationships between the topics. A first topic page for a first topic is presented in a user interface in the social network. The first topic page includes articles and first options for navigating to topic pages of topics related to the first topic. The method further includes detecting a selection of a first article. In response to detecting the selection, a first article page for the first article is presented in the user interface. The first article page includes details of the first article and second options for navigating to topic pages of topics related to the first article.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Large Scale Optimizing Engine. The Large Scale Optimizing Engine determines a probability, for each content item in a set of content items, of the respective member account performing a content item action. The Large Scale Optimizing Engine identifies a select content item from the set of content items based on determining display of the select content item will meet a first and second target. The Large Scale Optimizing Engine causes display of the select content item in a content slot in the respective member account's social network feed based on satisfaction of the first and second targets.

Abstract: A machine is configured to improve content recommendations. For example, the machine accesses a first score representing an affinity between a job description and a member profile. The first score is generated based on a first embedding that represents the job description, and includes a feature that identifies an organization associated with the job description, and a second embedding that represents the member profile. The machine, based on the first score exceeding a first threshold value, causes a display of a recommendation of the job description in a user interface. The machine, based on an indication of selection of the job description, generates a third embedding that represents an article associated with the organization. The machine generates a second score that represents a member profile-job affinity, and based on the second score exceeding a second threshold value, causes a display of a recommendation of the article in the user interface.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Fast Ranker Engine that identifies global model features present in an article in a social network service. The Fast Ranker Engine assembles respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model. The Fast Ranker Engine generates a transformation feature(s) for a prediction model of the article based on the respective fixed vectors.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Personalized Article Engine that generates respective prediction models for each article in a plurality of candidate articles in a social network system. The Personalized. Article Engine generates a respective article score according to each article's prediction model and at least one feature of a target member account. The Personalized Article Engine generates a plurality of output scores based on combining each respective article score with a corresponding article's global model score. The Personalized Article Engine ranks the output scores to identify a subset of candidate articles relevant to the target member account.

Abstract: A method may include determining a decision space representing a set of content items to be presented on a user interface of a social networking site, the decision space accounting for competing quadratic constraints and interaction effects, estimating the decision space to linearize the competing quadratic constraints, determining, in the estimated decision space and using an objective function, a display probability for each content item in the set of content items, each respective display probability corresponding to a given content item's probability of display in a specific content slot of a plurality of content slots on the user interface; and causing display of the content items with the highest display probabilities.

Abstract: In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.

Abstract: In an example embodiment, a generalized linear mixed effect model is trained using sample job posting results resulting from sample queries from sample members having sample member data. The generalized linear mixed effect model has coefficients based on a global ranking model as well as coefficients based on features from job posting results. The generalized linear mixed effect model may be trained to output application likelihood scores for each of a plurality of candidate job posting results produced by a query from a first member. The application likelihood scores may then be used to sort the candidate job posting results.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to Large Scale Optimizing Engine. The Large Scale Optimizing Engine determines a probability, for each content item in a set of content items, of the respective member account performing a content item action. The Large Scale Optimizing Engine identifies a select content item from the set of content items based on determining display of the select content item will meet a first and second target. The Large Scale Optimizing Engine causes display of the select content item in a content slot in the respective member account's social network feed based on satisfaction of the first and second targets.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Content Optimization Engine that determines a display probability for each content item in a set of content items. Each respective display probability corresponds to a given content item's probability of display in a specific content slot of a plurality of content slots in a social network feed of a target member account in a social network service. The Content Optimization Engine calculates a selection probability for each content item in an ordered set of the content items, based on each display probability and a set of interaction effects. The Content Optimization Engine causes display of the ordered set of content items in the target member account's social network feed based on satisfaction of the first and second targets.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Personalized Article Engine that generates respective prediction models for each article in a plurality of candidate articles in a social network system. The Personalized. Article Engine generates a respective article score according to each article's prediction model and at least one feature of a target member account. The Personalized Article Engine generates a plurality of output scores based on combining each respective article score with a corresponding article's global model score. The Personalized Article Engine ranks the output scores to identify a subset of candidate articles relevant to the target member account.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method as described herein are directed to a Fast Ranker Engine that identifies global model features present in an article in a social network service. The Fast Ranker Engine assembles respective fixed vectors based on at least one member account feature and each coefficient that corresponds to a present global article feature of the global model. The Fast Ranker Engine generates a transformation feature(s) for a prediction model of the article based on the respective fixed vectors.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for an article presented (or to be presented) in a heterogeneous feed of a social network.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which help determine a user's likely affinity for consuming content (such as an article) in a particular language presented (or to be presented) in a heterogeneous feed of a social network.