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: Techniques for using machine-learned models to throttle content are provided. In one technique, based on multiple selection events, a distribution of relevance measures is computed, where the relevance measures are associated with the content item selection events. The relevance measures may be generated by one or more machine-learned models. Based on the computed distribution, a threshold relevance measure is computed. Thereafter, a request for content is received over a computer network. In response, a computer system performs, in real-time, multiple steps. For example, an identity of an entity that is associated with the request is identified and, based on that identity, multiple content delivery groups are identified. A relevance measure of one of the content delivery groups relative to the entity is determined and compared to the threshold relevance measure. The content delivery group is selected only after determining that the relevance measure is above the threshold relevance measure.

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: 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, a machine-readable storage medium storing instructions, and a computer-implemented method described herein are directed to a Similar Profiles Engine. The Similar Profiles Engine generates an inverted index query based on one or more portions of profile data of a target member account of a social network service. The Similar Profiles Engine identifies respective profile data, of one or more candidate member accounts in the social network service, that maps to at least one inverted index filter, the at least one inverted index filter matching at least a portion of the inverted index query. The Similar Profiles Engine calculates a similarity score between each respective candidate member account and the target member account, and causes a display of identifiers of one or more candidate member accounts in a user interface of a client device based on respective similarity scores.

Abstract: A machine is configured to improve a search engine. For example, the machine generating, for a user, one or more search facets using one or more machine learning algorithms. The generating of the search facets is based on a user profile associated with the user and one or more similar user profiles. The machine receives an identifier of the user from a client device. The machine causes a display of one or more selectable identifiers of the one or more search facets in a user interface of the client device associated with the user. The machine receives, from the client device, an indication of a selection of the one or more selectable identifiers of the one or more search facets. The machine causes a display of one or more job descriptions in the user interface based on a search performed using the one or more search facets.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method described herein are directed to a Similar Profiles Engine. The Similar Profiles Engine generates an inverted index query based on one or more portions of profile data of a target member account of a social network service. The Similar Profiles Engine identifies respective profile data, of one or more candidate member accounts in the social network service, that maps to at least one inverted index filter, the at least one inverted index filter matching at least a portion of the inverted index query. The Similar Profiles Engine calculates a similarity score between each respective candidate member account and the target member account, and causes a display of identifiers of one or more candidate member accounts in a user interface of a client device based on respective similarity scores.

Abstract: Method and system are provided to determine a member profile associated with a reference to a person in a publication and may be used beneficially to identify members of a social network in news articles. The system may be configured to detect a publication that was shared by a member of an on-line social networking system, determine that the publication includes a name entity, and identify a member profile in the on-line social networking system that represents a member referenced by the name entity. The process of identifying a member profile in the on-line social networking system that represents a member referenced by the name entity may include building a list of candidate profiles from the member profiles of the on-line social networking system and selecting a matching profile from the candidate profiles, utilizing one or more disambiguation techniques.

Abstract: Method and system are provided to determine a member profile associated with a reference to a person in a publication and may be used beneficially to identify members of a social network in news articles. The system may be configured to detect a publication that was shared by a member of an on-line social networking system, determine that the publication includes a name entity, and identify a member profile in the on-line social networking system that represents a member referenced by the name entity. The process of identifying a member profile in the on-line social networking system that represents a member referenced by the name entity may include building a list of candidate profiles from the member profiles of the on-line social networking system and selecting a matching profile from the candidate profiles, utilizing one or more disambiguation techniques.

Abstract: Method and system are provided to determine a member profile associated with a reference to a person in a publication and may be used beneficially to identify members of a social network in news articles. The system may be configured to detect a publication that was shared by a member of an on-line social networking system, determine that the publication includes a name entity, and identify a member profile in the on-line social networking system that represents a member referenced by the name entity. The process of identifying a member profile in the on-line social networking system that represents a member referenced by the name entity may include building a list of candidate profiles from the member profiles of the on-line social networking system and selecting a matching profile from the candidate profiles, utilizing one or more disambiguation techniques.

Abstract: A technology is described for ranking content using user feedback. A method can include presenting a content entry to a plurality of users to enable viewing of the content entry. Positive and negative ratings can be captured about the content entry from the plurality of users. A relative deviation value can be calculated using the positive ratings and negative ratings for the content entry to form a raw rating score using a processor. Another operation can be scaling the raw rating score via a power function to form a controversial rating score using a processor. The content entry may then be displayed in a ranked order with other content entries based on the controversial rating score.