Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.

Our vision is to have industry-wide impact on how people build and apply machine learning technology. To realize this vision, we have to be a part of the machine learning community -- we have to share our code. While there are many open source machine learning libraries currently available, we feel that Photon ML is an important addition because of the direction we intend to take the library toward: generalized additive mixed effect models (GAME), described in more detail below. Currently, the GAME implementation in Photon ML supports generalized linear mixed effect models (GLMix), a subset of the algorithms we intend to one day support in GAME. A GLMix model consists of a fixed effect component and multiple random effects.