Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions

TL;DR

This paper introduces a role-aware expert mixture model combined with LLM-extracted fine-grained job descriptions to improve talent search ranking, achieving significant performance gains and cost savings in recruitment systems.

Contribution

It proposes a novel framework integrating LLMs and role-aware MoE networks for more precise talent search, addressing limitations of existing methods.

Findings

Achieved 1.70% CTR gain in online A/B tests

Realized 5.97% CVR improvement

Generated 17.29% increase in click-through conversions

Abstract

Talent search is a cornerstone of modern recruitment systems, yet existing approaches often struggle to capture nuanced job-specific preferences, model recruiter behavior at a fine-grained level, and mitigate noise from subjective human judgments. We present a novel framework that enhances talent search effectiveness and delivers substantial business value through two key innovations: (i) leveraging LLMs to extract fine-grained recruitment signals from job descriptions and historical hiring data, and (ii) employing a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles. To further reduce noise, we introduce a multi-task learning module that jointly optimizes click-through rate (CTR), conversion rate (CVR), and resume matching relevance. Experiments on real-world recruitment data and online A/B testing show relative AUC gains of 1.70% (CTR) and 5.97% (CVR), and a 17.29% lift in click-through conversion rate. These improvements reduce dependence on external sourcing channels, enabling an estimated annual cost saving of millions of CNY.