Weixin Chen

Applied Scientist at Kuaishou Technology

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

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

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

His research focuses on LLM-based agentic and generative recommender systems.

Recent News

2026

Awarded Top Reviewer at NeurIPS 2026.
Invited talk on Generative vs. Agentic Recommendation at Shenzhen University. [Slides]
One paper accepted by ACM TORS (co-first author).
One paper accepted to EMNLP 2026 Findings. Congrats to Zhen.
One paper accepted to KDD 2026. Congrats to Shu and Yuhan.
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).
One paper 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 papers
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 MemRec GitHub stars 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)}
}
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 arXiv 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)}
}
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}
}

Invited Talks

Generative vs. Agentic Recommendation:Two LLM Paradigms for Recommender Systems, and the MemRec Framework

Shenzhen University, Shenzhen · Invited by Prof. Weike Pan

Research Experience

Education

Selected Honors & Awards

More honors & awards
  • Excellent Teaching Assistant Performance Award, HKBU (2023-2024)
  • Teaching Assistant Performance Award (3 times), HKBU (2022-2025)
  • Academic Performance Award, SZU (2017-2019)
  • Outstanding Contribution of Student Lecturer (2 times), SZU (2017, 2018)
  • Star of SZU (Highest Honor in Major), SZU (2017)

Academic Service

Contact Me