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Entity Personalized Talent Search Models with Tree Interaction Features
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.Comment: This paper has been accepted for publication at ACM WWW 201
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