Abstract: An anti-theft device for protecting a portable electronic device from theft or unauthorized removal in a retail environment is provided. The anti-theft device includes a shroud configured to at least partially receive and engage a portable electronic device. The anti-theft device also includes a dock configured to releasably engage the shroud. The dock is configured to engage the shroud in a locked configuration so as to prevent removal of the shroud and the portable electronic device from the dock and to disengage the shroud in an unlocked configuration so as to allow the shroud and the portable electronic device to be removed from the dock.

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: The disclosed embodiments provide a system for processing data. During operation, the system determines impression discounting features for ordering a set of candidates that match parameters of a search from a recruiter, wherein the impression discounting features include a recruiter-candidate feature indicating interaction between the recruiter and a candidate and a candidate popularity feature indicating interaction between the candidate and a set of recruiters. Next, the system applies a machine learning model to the impression discounting features and features for the set of candidates to produce a first set of scores for personalizing a ranking of the set of candidates for the recruiter. The system then generates the ranking according to the first set of scores. Finally, the system outputs, to the recruiter, at least a portion of the ranking as search results of the search.