用大模型优化招聘匹配，提升精准度并省下千万招聘成本。
Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation
- 用大模型重写低质量职位描述，提升数据质量
- 引入类别感知专家混合模型，更好识别相似求职-职位对
- 在线测试点击转化率提升19.4%，节省千万人力成本
人员-岗位匹配（PJF）是在线招聘的核心。现有方法在处理低质量职位描述和相似求职-职位对时表现不佳。本文提出一种基于大语言模型（LLM）的方法，包含两项创新：（1）基于链式思维提示的LLM数据增强，用于优化和重写低质量职位描述；（2）类别感知的专家混合（MoE）模块，通过类别嵌入动态分配专家权重，学习更可区分的相似配对模式。我们在招聘平台进行离线评估与在线A/B测试，结果表明，该方法相较现有方法在AUC上提升2.40%，GAUC提升7.46%，在线测试中点击率转化率（CTCVR）提升19.4%，每年节省数百万人民币外部猎头支出。
原文摘要 · Abstract (English)
Person-Job Fit (PJF) is a critical component for online recruitment. Existing approaches face several challenges, particularly in handling low-quality job descriptions and similar candidate-job pairs, which impair model performance. To address these challenges, this paper proposes a large language model (LLM) based method with two novel techniques: (1) LLM-based data augmentation, which polishes and rewrites low-quality job descriptions by leveraging chain-of-thought (COT) prompts, and (2) category-aware Mixture of Experts (MoE) that assists in identifying similar candidate-job pairs. This MoE module incorporates category embeddings to dynamically assign weights to the experts and learns more distinguishable patterns for similar candidate-job pairs. We perform offline evaluations and online A/B tests on our recruitment platform. Our method relatively surpasses existing methods by 2.40% in AUC and 7.46% in GAUC, and boosts click-through conversion rate (CTCVR) by 19.4% in online tests, saving millions of CNY in external headhunting expenses.
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