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: The disclosed technologies include a navigation agent for a search interface. In an embodiment, the navigation agent uses reinforcement learning to dynamically generate and select navigation options for presentation to a user during a search session. The navigation agent selects navigation options based on reward scores, which are computed using implicit and/or explicit user feedback received in response to presentations of navigation options.

Abstract: The disclosed embodiments provide a system for processing searches. During operation, the system determines features related to attributes of candidates and interactions of the candidates with an online system. Next, the system applies a static ranking machine learning model to the features to produce scores representing likelihoods of outcomes related to the candidates and stores rankings of the candidates by descending values of the scores in entries of an inverted index. During processing of a search of the candidates in the online system, the system retrieves a subset of the candidates with the values of the scores that exceed a threshold from a subset of the entries in the inverted index that match parameters of the search. Finally, the system aggregates the retrieved subset of candidates for use in subsequent ordering of the subset of candidates by one or more dynamic ranking models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines activity features for candidates that match parameters of a search, wherein the activity features include a first amount of time since a most recent visit by a candidate to an online platform used to conduct interaction between the candidate and moderators of opportunities. Next, the system applies a machine learning model to the activity features and candidate features for the candidates to produce a first set of scores between the candidates and the parameters. The system then generates a ranking of the candidates according to the first set of scores. Finally, the system outputs at least a portion of the ranking as search results of the search.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies a positive action by an entity on a candidate as a result of a query performed by the entity for a ranking of candidates. Next, the system identifies related queries that occur within a time window preceding the query. The system then generates positive labels associated with the candidate and one or more related queries that produce rankings containing the candidate. Finally, the system outputs the positive labels in training data for a machine learning model that generates the rankings.

Abstract: A computing device can generate a collection of phrases using both authoritative data and behavioral data, for example, using previously submitted search queries. The collection of phrases can be used, in part, to determine the best segmentation of search queries. Each segmentation of a search query splits the terms in search query using different permutations or n-grams to identify one or more phrases. Each segmentation is scored based on various criteria. The segmentation having the highest score is included in training data for training a predictive model that predicts segmentations for new search queries. The predicted segmentation can be used to annotate that query to identify the one or more phrases that were created by the segmentation of the query. The annotated query can be processed, for example, by a search engine, to obtain resources that are responsive to the one or more phrases that were identified by the segmentation.