Abstract: The disclosed embodiments provide a method, apparatus, and system for training and using optimizing down funnel predictions using machine-learned labels. More particularly, rather than using a single machine-learned model to predict whether an event (e.g., whether a user will be hired for a particular job) will occur, two separately trained machine-learned models are used. The first model (called the “label model”) is used to create labels for data items (e.g., user profiles and/or other user information, job listing information, etc.) that are obtained, but where it is not known yet whether the event has occurred. These labels may then be combined with those data items and used to train the second model (called the “prediction model”) to learn how to predict whether the event will occur for a data item passed to it.

Abstract: Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

Abstract: Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.

Abstract: A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.

Abstract: Techniques for suggesting targeting criteria for a content delivery campaign are provided. An affinity score representing an affinity between the attribute values of each pair of multiple pairs of attribute values is computed. First input indicating a particular attribute value for a particular attribute type is received through a user interface for creating a content delivery campaign. The user interface includes fields for inputting attribute values for multiple attribute types that includes the particular attribute type. In response to the first input and based on affinity scores associated with the particular attribute value, a set of suggested attribute values is identified. The user interface is updated to include the set of suggested attribute values. Second input indicating a selection of a particular suggested attribute value is received. The particular suggested attribute value is added to the content delivery campaign.

Abstract: A system and method for automatic topic tagging are provided. In example embodiments, input content is received, the content includes a plurality of terms. Term vectors are generated from the plurality of terms. Candidate topics are identified to assigned to the plurality of terms. Topics are assigned to the received content from the identified candidate topics.

Abstract: A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.

Abstract: A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.

Abstract: A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.

Abstract: A system and method for automatic topic tagging are provided. In example embodiments, input content is received, the content includes a plurality of terms. Term vectors are generated from the plurality of terms. Candidate topics are identified to assigned to the plurality of terms. Topics are assigned to the received content from the identified candidate topics.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein to a Discussion Relevance Engine that filters a plurality of discussions in a social network to identify a discussion pool. The Discussion Relevance Engine identifies a plurality of eligible discussions in the discussion pool, wherein each eligible discussion corresponds to a respective social network member group to which a target member account has previously subscribed. The Discussion Relevance Engine calculates, for each eligible discussion, a relevance score predictive of a relevance of the eligible discussion to the target member account. The Discussion Relevance Engine recommends at least one of the eligible discussions to the target member account based at least in part on the calculated relevance scores.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are directed to a Digest Engine that identifies a feature(s) that is predictive of relevance, to a target member account in a professional social network, of content from a member group(s) to which the target member account is subscribed. Based on the feature(s), the Digest Engine determines a portion(s) of relevant content created amongst respective member accounts subscribed to the member group(s). The Digest Engine generates a persistent message providing access to the portion(s) of relevant content. The Digest Engine sends the persistent message to the target member account.