@inproceedings{chen-etal-2026-enhancing,
    title = "Enhancing Online Recruitment with Category-Aware {M}o{E} and {LLM}-based Data Augmentation",
    author = "Chen, Minping  and
      Xu, Bing  and
      Chen, Zulong  and
      Xu, Chuanfei  and
      Zhou, Ying  and
      Tao, Zui  and
      Wen, Zeyi",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.55/",
    pages = "812--824",
    ISBN = "979-8-89176-394-4",
    abstract = "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."
}