Abstract: Embodiments of the present invention are directed to grouping clients in a work marketplace into clusters such that, in each cluster, clients are similar with respect to their hiring criteria. In some embodiments, the clusters are generated based on a clustering algorithm that can be applied effectively on large datasets. This separation allows the work marketplace to discover differences in client hiring criteria, to learn more accurately the hiring criteria in each cluster and to recommend the right contractor to each client for a successful collaboration, thereby improving matching between clients and contractors in the work marketplace. For each contractor who have submitted an application to a project posted by a client, a contractor/opening pair score is determined based on information of the cluster that is associated with the client. The contractor/opening pair score is used to ascertain whether the contractor would be a candidate recommended to the client.

Abstract: Embodiments of the present invention are directed to a classification engine that classifies freelancers in one of a plurality of categories. The classification engine captures data to perform a first classification evaluation of each engaged freelancer. The first classification evaluation is used to drive different levels of onboarding to ensure appropriate onboarding tasks are completed for each engaged freelancer before the freelancer starts work for a client. Depending on the level of onboarding, the classification engine either uses the first classification evaluation to make a determination regarding the classification of the freelancer or captures additional data to perform a second classification evaluation to make the determination. The classification engine eliminates delays, manual workarounds and helps scale work with enterprise clients.