Entity Personalized Talent Search Models with Tree Interaction Features
This work addresses the problem of improving recommendation accuracy for recruiters in talent search systems, though it appears incremental as it builds on existing hybrid modeling approaches.
The paper tackles the lack of user-level personalization in talent search systems by proposing an entity-personalized model combining GLMix and GBDT models with tree interaction features, resulting in significant improvements in precision metrics compared to non-personalized models.
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.