# Entity Personalized Talent Search Models with Tree Interaction Features

## TL;DR

This paper introduces an entity-personalized talent search model combining GLMix and GBDT, leveraging tree interaction features to improve candidate recommendations tailored to individual recruiters.

## Contribution

It presents a novel hybrid model integrating personalized linear mixed models with nonlinear GBDT features for talent search, along with system architecture and empirical validation.

## Key findings

- Significant improvement in precision metrics over non-personalized models.
- Effective online and offline deployment of the hybrid model.
- Enhanced personalization in talent recommendations.

## Abstract

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.