用大模型提升招聘匹配精度，让系统更懂岗位和招聘者差异。
Enhancing Talent Search Ranking with Role-Aware Expert Mixtures and LLM-based Fine-Grained Job Descriptions
- 用大模型解析职位描述与历史数据，提取细粒度招聘信号。
- 角色感知的多门控MoE网络捕捉不同招聘角色的行为差异。
- 多任务学习降低主观噪声，提升点击转化率17.29%。
人才搜索是现代招聘系统的核心，但现有方法难以捕捉岗位特异性偏好、精细化建模招聘行为，且易受主观判断噪声影响。本文提出新框架，通过两大创新提升招聘效果并带来显著商业价值：(i) 利用大语言模型从职位描述和历史招聘数据中提取细粒度招聘信号；(ii) 采用角色感知的多门控MoE网络，捕捉不同招聘角色的行为差异。为减少噪声，引入多任务学习模块，联合优化点击率（CTR）、转化率（CVR）与简历匹配相关性。在真实招聘数据及线上A/B测试中，相对AUC提升1.70%（CTR）和5.97%（CVR），点击转化率提升17.29%。该优化降低了对外部招聘渠道的依赖，预计每年节省数百万人民币成本。
原文摘要 · Abstract (English)
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.
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