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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.21264 |
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| _version_ | 1866910159164407808 |
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| author | Chen, Minping Xu, Bing Chen, Zulong Xu, Chuanfei Zhou, Ying Tao, Zui Wen, Zeyi |
| author_facet | Chen, Minping Xu, Bing Chen, Zulong Xu, Chuanfei Zhou, Ying Tao, Zui Wen, Zeyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21264 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation Chen, Minping Xu, Bing Chen, Zulong Xu, Chuanfei Zhou, Ying Tao, Zui Wen, Zeyi Artificial Intelligence 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. |
| title | Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.21264 |