Abstract: Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which may be used in conjunction with a campaign for distributing content to users of the social network. Among other things, embodiments of the present disclosure provide a number of advantages over conventional systems for content distribution, including a simplified targeting process and increased reach (i.e. distribution) for content providers among users of a social network, as well as improving the utilization of an inventory of content and higher and more efficient engagement with such content by users of the social network.

Abstract: Herein are techniques to use an artificial neural network to score the relevance of content items for a target and techniques to rank the content items based on their scores. In embodiments, a computer uses a plurality of expansion techniques to identify expanded targets for a content item. For each of the expanded targets, the computer provides inputs to an artificial neural network to generate a relevance score that indicates a relative suitability of the content item for that target. The computer ranks the expanded targets based on the relevance score generated for each of the expanded targets. Based on the ranking, the computer selects a subset of targets from the available expanded targets as the expanded targets for whom the content item is potentially most relevant. The computer stores an association between the content item and each target in the subset of expanded targets.

Abstract: Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.

Abstract: Herein are techniques to use an artificial neural network to score the relevance of content items for a target and techniques to rank the content items based on their scores. In embodiments, a computer uses a plurality of expansion techniques to identify expanded targets for a content item. For each of the expanded targets, the computer provides inputs to an artificial neural network to generate a relevance score that indicates a relative suitability of the content item for that target. The computer ranks the expanded targets based on the relevance score generated for each of the expanded targets. Based on the ranking, the computer selects a subset of targets from the available expanded targets as the expanded targets for whom the content item is potentially most relevant. The computer stores an association between the content item and each target in the subset of expanded targets.

Abstract: The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which may be used in conjunction with a campaign for distributing content to users of the social network. Among other things, embodiments of the present disclosure provide a number of advantages over conventional systems for content distribution, including a simplified targeting process and increased reach (i.e. distribution) for content providers among users of a social network, as well as improving the utilization of an inventory of content and higher and more efficient engagement with such content by users of the social network.

Abstract: Systems and methods are disclosed that recommend one or more electronic presentations to a user based on one or more factors. These factors may include contextual information, behavioral information, profile information, or combinations of the foregoing. Contextual information may include content and/or features extracted from a given electronic presentation. Behavioral information may include user behavioral data, such as the number of times a user has viewed a presentation, the amount of the presentation viewed by the user, presentations previously viewed by the user, and other such behavioral data. Profile information may include user professional profile information, such as skills the user has identified as possessing, employment history information, and other such user professional profile information.

Abstract: Techniques for assisting a user in determining an interest level between a member of a social network system and an organization. According to various embodiments, applicant data is accessed for applicants having applied to an organization. A set of common applicant characteristics is determined for the set of applicant data. Member data is accessed indicative of a member of an online social media network. An interest score is generated based on a comparison of the member data and the set of applicant data. An identification of the organization is presented based on the interest score.

Abstract: In order to expand the audience for an advertising campaign, a system identifies an expanded audience for the advertising campaign based on characteristics of individuals in the expanded audience and a target audience of the advertising campaign. Then, the system compares a historical cumulative advertising performance metric at a current time for the target audience with a current cumulative advertising performance metric at the current time for the target audience in the advertising campaign. Next, the system selectively changes a probability of showing advertisements in the advertising campaign to individuals in the expanded audience based on the comparison. For example, if a current cumulative number of daily advertising impressions at the current time is less than a historical cumulative number of daily advertising impressions at a current time, the system increases the probability.

Abstract: The disclosed systems and methods are directed to detecting spam in an electronic presentation and determining whether the electronic presentation should be moderated. The example systems and methods may employ one or more classifiers for classifying an electronic presentation and, should the electronic presentation fall within a predetermined classification, the electronic presentation may be analyzed further for the presence of spam. Further analysis of the electronic presentation may include invoking one or more filters to determine whether the electronic presentation includes words and/or phrases known to be associated with spam. Where the electronic presentation is determined to contain spam, the electronic presentation may be removed from a database of electronic presentations, excluded from search results, or flagged for moderation by a moderator.

Abstract: Systems and methods are disclosed that recommend one or more electronic presentations to a user based on one or more factors. These factors may include contextual information, behavioral information, profile information, or combinations of the foregoing. Contextual information may include content and/or features extracted from a given electronic presentation. Behavioral information may include user behavioral data, such as the number of times a user has viewed a presentation, the amount of the presentation viewed by the user, presentations previously viewed by the user, and other such behavioral data. Profile information may include user professional profile information, such as skills the user has identified as possessing, employment history information, and other such user professional profile information.

Abstract: A system, apparatus, method and computer-program product are provided for determining affinities between members of an on-line service and/or one member's likely propensity for content published by or on behalf of another member. Members of the service include individuals and organizations. A content item may be an announcement by or for a member, an advertisement, a job listing or something else. Content items available for service to an individual member are ranked based on the member's propensity for consuming them, as reflected in scores computed for each item. An item's propensity score may be calculated based on the relevance and/or proximity between the member and an organization featured in or associated with the item. Relevance may measure the similarity between profiles of the individual and the organization. Proximity may be affected by whether the individual and/or associates of the individual follow the organization, visit a page of the organization, etc.