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 performing prefetching for a ranking service in a microservice architecture are provided. In one technique, in response to receiving a content request, an entity identifier of an entity associated with the content request is determined, a host of a second service that is different than the first service is determined. The first service sends the entity identifier to the host of the second service. The second service retrieves entity feature data that is associated with the entity identifier. The first service identifies a set of content delivery campaigns, identifies the host of the second service, and sends the identity of the set of content delivery campaigns to the host of the second service. The host of the second service determines a ranking of the set of content delivery campaigns, a subset thereof is selected, and data about each selected campaign is transmitted over a computer network.

Abstract: Techniques for performing prefetching for a ranking service in a microservice architecture are provided. In one technique, in response to receiving a content request, an entity identifier of an entity associated with the content request is determined, a host of a second service that is different than the first service is determined. The first service sends the entity identifier to the host of the second service. The second service retrieves entity feature data that is associated with the entity identifier. The first service identifies a set of content delivery campaigns, identifies the host of the second service, and sends the identity of the set of content delivery campaigns to the host of the second service. The host of the second service determines a ranking of the set of content delivery campaigns, a subset thereof is selected, and data about each selected campaign is transmitted over a computer network.

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: In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.

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 are described herein to a Group Relevance Engine that generates, for a group in a social network, an aggregate group feature based on a common attribute shared amongst member accounts currently subscribed to the group. The Group Relevance Engine identifies an account feature corresponding to the common attribute in a profile of a target member account. The Group Relevance calculates a relevance score based at least on a match between the aggregate group feature and the account feature. The Group Relevance determines whether to recommend the group to the target member account based at least on the relevance score.