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 from a moderator of an opportunity, wherein the activity features include an amount of interaction between a candidate and additional moderators and a frequency of visits by the candidate to a platform used to conduct the interaction between the candidate and the additional moderators. Next, the system applies a machine learning model to the activity features to produce activeness scores representing levels of activity of the candidates with respect to the platform. The system then generates a ranking of the candidates according to the activeness scores. Finally, the system outputs at least a portion of the ranking as a set of search results of the search.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system identifies candidates for opportunities in a domain based on communication between the candidates and moderators of the opportunities. Next, the system determines a set of skills as representative of talent in the domain based on occurrences of the set of skills in attributes of the candidates. The system then identifies additional candidates with experience in the domain based on overlap between additional attributes of the additional candidates and the set of skills. Finally, the system outputs the additional candidates as recommendations to additional moderators of additional opportunities in the domain.