Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first embedding generated by a topic model from parameters of searches by a first recruiting entity and obtains a set of additional embeddings generated by the topic model from attributes of a set of candidates. Next, the system determines match features that include measures of similarity between the first embedding and each embedding in the set of additional embeddings. The system then applies a machine learning model to the match features and additional features for the candidates to produce a set of scores for the candidates. Finally, the system generates a ranking of the candidates according to the scores and outputs at least a portion of the ranking as search results of a current search by the first recruiting entity.

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 obtains a first embedding generated by a topic model from parameters of searches by a first recruiting entity and obtains a set of additional embeddings generated by the topic model from attributes of a set of candidates. Next, the system determines match features that include measures of similarity between the first embedding and each embedding in the set of additional embeddings. The system then applies a machine learning model to the match features and additional features for the candidates to produce a set of scores for the candidates. Finally, the system generates a ranking of the candidates according to the scores and outputs at least a portion of the ranking as search results of a current search by the first recruiting entity.

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: Disclosed herein are systems, methods, and non-transitory computer-readable media for providing user notifications based on a project context. The system may receive candidate attributes from candidate devices of a plurality of candidates and storing them in a candidate database, as well as user-entered attributes from a user device of a user. The system may then iteratively execute a number of operations that include performing a search for candidates in the candidate database by comparing project attributes with candidate attributes and providing user notification of newly-matched candidates that includes returning returned candidates that are matching candidates of the search results to the user based on the search.

Abstract: Systems and methods for query expansion are disclosed. In some examples, a server receives, from a client device, a search query for employment candidates, the search query comprising a first set of parameters. The server determines a second set of parameters related to the first set of parameters in response to identifying a second parameter for the second set of parameters that corresponds with a first parameter from the first set of parameters, the professional records being stored in a professional data repository. The server generates, from the professional data repository, a first set of search results based on the first set of parameters and the second set of parameters. The server provides, to the client device, an output representing the first set of search results.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which provide for retrieval, ranking, and display of candidates that are more likely to respond to employment inquiries in an employment search graphical user interface (GUI). The system may employ a machine learning algorithm which may calculate a score for each member of the social networking service that predicts, based upon one or more features how likely the individual is to respond to a message. In some examples, the candidates that are determined to be more likely to respond may be presented as a selectable option in the GUI.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums which provide for retrieval, ranking, and display of candidates that are more likely to respond to employment inquiries in an employment search graphical user interface (GUI). The system may employ a machine learning algorithm which may calculate a score for each member of the social networking service that predicts, based upon one or more features how likely the individual is to respond to a message. In some examples, the candidates that are determined to be more likely to respond may be presented as a selectable option in the GUI.

Abstract: An adapter (150) for an earpiece (100) is provided to convert an on-ear device to an in-ear device. The adapter (150) is formed of a base (152) having clips (154) and an ear tip (156) extending therefrom. The base (150) clips onto the on-ear device (100) such that porting on the on-ear device is channeled through the ear tip (156) as an in-ear device.