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

arXiv: https://arxiv.org/abs/2512.00004 · HTML: https://mlsanity.com/paper/2512.00004/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Multi-Pointer Co-Attention Networks for Recommendation](https://mlsanity.com/paper/1801.09251/) (2018)
- [Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://mlsanity.com/paper/2311.04635/) (2023)
- [Towards Deep and Representation Learning for Talent Search at LinkedIn](https://mlsanity.com/paper/1809.06473/) (2018)
- [STEM: Unleashing the Power of Embeddings for Multi-task Recommendation](https://mlsanity.com/paper/2308.13537/) (2023)
- [Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned](https://mlsanity.com/paper/1809.06481/) (2018)
- [Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn](https://mlsanity.com/paper/1602.08186/) (2016)
- [From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach](https://mlsanity.com/paper/1709.00653/) (2017)
- [In-Session Personalization for Talent Search](https://mlsanity.com/paper/1809.06488/) (2018)
- [Entity Personalized Talent Search Models with Tree Interaction Features](https://mlsanity.com/paper/1902.09041/) (2019)

## Built on by (most cited first, 2 of 2)

- [OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation](https://mlsanity.com/paper/2602.08896/) (2026)
- [Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals](https://mlsanity.com/paper/2610.03112/) (2026)

## Abstract (arXiv)

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.