Weixin Chen

Email: chenweixin@kuaishou.com (work), me@weixinchen.com (general purpose)

I'm an Applied Scientist at Kuaishou Technology, working on LLM-based recommendation through the Kuaishou K-Star Talent Program (快Star-X).

I did my Ph.D. at Hong Kong Baptist University, advised by Prof. Li Chen. I was a Visiting Scholar at Rutgers University, working with Prof. Yongfeng Zhang. I obtained my B.Eng. from Shenzhen University, advised by Prof. Weike Pan.

My research interests focus on LLM-based agentic and generative recommender systems.

Recent News

2026

Invited talk on Generative vs. Agentic Recommendation at Shenzhen University. [Slides]
SC-VUG accepted by ACM TORS (co-first author).
SAGER accepted to EMNLP 2026 Findings.
One paper accepted to KDD 2026.
MemRec accepted to ACL 2026 (Main Conference).
Invited talk on MemRec at Kuaishou, Beijing. [Slides]
Invited talk on MemRec at Tencent, Shenzhen. [Slides]
One paper accepted to HCRS @ WWW 2026 (Oral).
MemRec preprint is available on arXiv.
One paper accepted to WWW 2026 (co-first author).

2025

Presented at RecSys 2025 in Prague. Check our [Paper] and [Video].
Joined Rutgers WISE Lab as a visiting student researcher.
VUG accepted to RecSys 2025 (Spotlight Oral).
FMMRec accepted by ACM TOIS.
One paper accepted by ACM TORS.

2022

Presented at RecSys 2022 remotely. Check our [Paper] and [Video].
Started my Ph.D. at HKBU CS.
One paper accepted to RecSys 2022 (Oral).

Selected Publications

(* indicates equal contributions)

All topics ▼
Total citations: --
MemRec Model
ACL
2026
MemRec: Collaborative Memory-Augmented Agentic Recommender System
W. Chen, Y. Zhao, J. Huang, Z. Ye, C. M. Ju, T. Zhao, N. Shah, L. Chen, Y. Zhang.
ACL 2026 PDF Code Demo BibTeX
🏆 Recognized as "the strongest competitor" among LLM-based methods by Meta's MARS paper.
@inproceedings{chen2026memrec,
  title   = {MemRec: Collaborative Memory-Augmented Agentic Recommender System},
  author  = {Chen, Weixin and Zhao, Yuhan and Huang, Jingyuan and Ye, Zihe and Ju, Clark Mingxuan and Zhao, Tong and Shah, Neil and Chen, Li and Zhang, Yongfeng},
  year    = 2026,
  booktitle = {Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2026)}
}
DivCDSR Model
KDD
2026
DivCDSR: A Model-Agnostic Framework for Diverse Cross-Domain Sequential Recommendation
S. Chen*, Y. Zhao*, W. Chen, W. Pan, L. Chen.
KDD 2026 BibTeX
@inproceedings{chen2026divcdsr,
  title     = {DivCDSR: A Model-Agnostic Framework for Diverse Cross-Domain Sequential Recommendation},
  author    = {Chen, Shu and Zhao, Yuhan and Chen, Weixin and Pan, Weike and Chen, Li},
  year      = 2026,
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '26)}
}
SAGER Model
EMNLP
2026
SAGER: Self-Evolving User Policy Skills for Recommendation Agent
Z. Tao, R. Lai, C. Yu, W. Chen, L. Chen, B. Kong, L. Cheng, C. Zhuo, Z. Li, Q. Sun.
EMNLP Findings 2026 PDF BibTeX
@inproceedings{tao2026sager,
  title     = {SAGER: Self-Evolving User Policy Skills for Recommendation Agent},
  author    = {Tao, Zhen and Lai, Riwei and Yu, Chenyun and Chen, Weixin and Chen, Li and Kong, Beibei and Cheng, Lei and Zhuo, Chengxiang and Li, Zang and Sun, Qingqiang},
  year      = 2026,
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026}
}
CDFA Model
WWW
2026
The Double-Edged Sword of Knowledge Transfer: Diagnosing and Curing Fairness Pathologies in Cross-Domain Recommendation
Y. Zhao*, W. Chen*, L. Chen, W. Pan.
WWW 2026 Oral PDF Code BibTeX
@inproceedings{zhao2026double,
  title     = {The Double-Edged Sword of Knowledge Transfer: Diagnosing and Curing Fairness Pathologies in Cross-Domain Recommendation},
  author    = {Zhao, Yuhan and Chen, Weixin and Chen, Li and Pan, Weike},
  year      = 2026,
  booktitle = {Proceedings of the ACM Web Conference 2026 (WWW '26)}
}
Cofair Model
HCRS@WWW
2026
Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation
W. Chen, L. Chen, Y. Zhao.
WWW 2026 HCRS Workshop Oral PDF Code BibTeX
@inproceedings{chen2026posttraining,
  title     = {Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation},
  author    = {Chen, Weixin and Chen, Li and Zhao, Yuhan},
  year      = 2026,
  booktitle = {Companion Proceedings of the ACM Web Conference}
}
SC-VUG Model
TORS
2026
SC-VUG: Fairness-Aware Cross-Domain Recommendation for Non-Overlapping Users via Sparsity-Calibrated Virtual User Generation
Y. Zhao*, W. Chen*, L. Chen, W. Pan.
ACM TORS PDF Code BibTeX
@article{zhao2026scvug,
  title     = {SC-VUG: Fairness-Aware Cross-Domain Recommendation for Non-Overlapping Users via Sparsity-Calibrated Virtual User Generation},
  author    = {Zhao, Yuhan and Chen, Weixin and Chen, Li and Pan, Weike},
  year      = 2026,
  journal   = {ACM Transactions on Recommender Systems},
  doi       = {10.1145/3841183},
  note      = {Article 3841183}
}
H-Mem Model
EACL
2026
H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents
Z. Ye, J. Huang, W. Chen, Y. Zhang.
EACL 2026 PDF BibTeX
@inproceedings{ye2026hmem,
  title     = {H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents},
  author    = {Ye, Zihe and Huang, Jingyuan and Chen, Weixin and Zhang, Yongfeng},
  year      = 2026,
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL'26)},
  pages     = {7756--7775}
}
VUG Model
RecSys
2025
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
W. Chen*, Y. Zhao*, L. Chen, W. Pan.
RecSys 2025 Spotlight Oral PDF Code Slides Poster Video BibTeX
@inproceedings{chen2025leave,
  title     = {Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users},
  author    = {Chen, Weixin and Zhao, Yuhan and Chen, Li and Pan, Weike},
  year      = 2025,
  booktitle = {Proceedings of the 19th ACM Conference on Recommender Systems (RecSys'25)},
  pages     = {226--236}
}
FMMRec Model
TOIS
2025
Causality-Inspired Fair Representation Learning for Multimodal Recommendation
W. Chen, L. Chen, Y. Ni, Y. Zhao.
ACM TOIS PDF Code BibTeX
@article{chen2025causality,
  title     = {Causality-Inspired Fair Representation Learning for Multimodal Recommendation},
  author    = {Chen, Weixin and Chen, Li and Ni, Yongxin and Zhao, Yuhan},
  year      = 2025,
  journal   = {ACM Transactions on Information Systems},
  volume    = {43},
  number    = {6},
  articleno = {153},
  numpages  = {29}
}
OtPr Fairness Model
TORS
2025
Investigating User-Side Fairness in Outcome and Process for Multi-Type Sensitive Attributes in Recommendations
W. Chen, Y. Zhao, L. Chen.
ACM TORS PDF Code BibTeX
@article{chen2025investigating,
  title     = {Investigating User-side fairness in outcome and process for multi-type sensitive attributes in recommendations},
  author    = {Chen, Weixin and Chen, Li and Zhao, Yuhan},
  year      = 2025,
  journal   = {ACM Transactions on Recommender Systems},
  volume    = {4},
  number    = {2},
  articleno = {25},
  numpages  = {29}
}
GPG4HSR Model
RecSys
2022
Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions
W. Chen, M. He, Y. Ni, W. Pan, Z. Ming, L. Chen.
RecSys 2022 Oral PDF Code Video BibTeX
@inproceedings{chen2022global,
  title     = {Global and personalized graphs for heterogeneous sequential recommendation by learning behavior transitions and user intentions},
  author    = {Chen, Weixin and He, Mingkai and Ni, Yongxin and Pan, Weike and Chen, Li and Ming, Zhong},
  year      = 2022,
  booktitle = {Proceedings of the 16th ACM Conference on Recommender Systems (RecSys'22)},
  pages     = {268--277}
}
MRL Model
TIST
2026
Matryoshka Representation Learning for Recommendation with Layer- and Hardness-Adaptive Negative Sampling
R. Lai, L. Chen, W. Chen, R. Chen.
ACM TIST PDF Code BibTeX
@article{lai2026matryoshka,
  title   = {Matryoshka Representation Learning for Recommendation with Layer- and Hardness-Adaptive Negative Sampling},
  author  = {Lai, Riwei and Chen, Li and Chen, Weixin and Chen, Rui},
  year    = 2026,
  journal = {ACM Transactions on Intelligent Systems and Technology},
}

Invited Talks

Generative vs. Agentic Recommendation: Two LLM Paradigms for Recommender Systems, and the MemRec Framework
Shenzhen University Shenzhen
2026.09
MemRec: Collaborative Memory-Augmented Agentic Recommender System
Beijing · Shenzhen · HKBU Hong Kong
2026.03 – 04

Research Experience

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Honors & Awards

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