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Talent Search systems aim to recommend potential candidates who are a goodmatch to the hiring needs of a recruiter expressed in terms of the recruiter'ssearch query or job posting. Past work in this domain has focused on linear andnonlinear models which lack preference personalization in the user-level due tobeing trained only with globally collected recruiter activity data. In thispaper, we propose an entity-personalized Talent Search model which utilizes acombination of generalized linear mixed (GLMix) models and gradient boosteddecision tree (GBDT) models, and provides personalized talent recommendationsusing nonlinear tree interaction features generated by the GBDT. We alsopresent the offline and online system architecture for the productionization ofthis hybrid model approach in our Talent Search systems. Finally, we provideoffline and online experiment results benchmarking our entity-personalizedmodel with tree interaction features, which demonstrate significantimprovements in our precision metrics compared to globally trainednon-personalized models.

Entity Personalized Talent Search Models with Tree Interaction Features