@inproceedings{li-etal-2025-enhancing-talent,
    title = "Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and {LLM}-based Fine-Grained Job Descriptions",
    author = "Li, Jihang  and
      Xu, Bing  and
      Chen, Zulong  and
      Xu, Chuanfei  and
      Chen, Minping  and
      Liu, Suyu  and
      Zhou, Ying  and
      Wen, Zeyi",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.13/",
    pages = "185--194",
    ISBN = "979-8-89176-333-3",
    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."
}