Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation

Computer Science > Artificial Intelligence [Submitted on 23 Apr 2026] Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation Minping Chen, Bing Xu, Zulong Chen, Chuanfei Xu, Ying Zhou, Zui Tao, Zeyi Wen 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. Comments: Accepted to ACL Industry Track 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.21264 [cs.AI]   (or arXiv:2604.21264v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21264 Focus to learn more Submission history From: Minping Chen [view email] [v1] Thu, 23 Apr 2026 04:17:04 UTC (675 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)