@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation
%A Chen, Minping
%A Xu, Bing
%A Chen, Zulong
%A Xu, Chuanfei
%A Zhou, Ying
%A Tao, Zui
%A Wen, Zeyi
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F chen-etal-2026-enhancing
%X 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.
%U https://aclanthology.org/2026.acl-industry.55/
%P 812-824
Markdown (Informal)
[Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation](https://aclanthology.org/2026.acl-industry.55/) (Chen et al., ACL 2026)
ACL
- Minping Chen, Bing Xu, Zulong Chen, Chuanfei Xu, Ying Zhou, Zui Tao, and Zeyi Wen. 2026. Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 812–824, San Diego, California, USA. Association for Computational Linguistics.