Xiao Chen

Ph.D. Candidate · The Hong Kong Polytechnic University

I am a Ph.D. candidate at The Hong Kong Polytechnic University, where I am fortunate to be advised by Professor Qing Li and Professor Zhaoxiang Zhang. I obtained my M.S. with honors from ZJU CS. My research centers on trustworthy personalization: building personalization systems that users can fully trust.

One ultimate goal of my PhD work is to build general personalization systems capable of learning continuously online once deployed (no taking it offline, no separation of pretraining and finetuning — just learning as it interacts with the world).

I have interned at Tencent and Amazon previously.

Current research interests: Lifelong Learning for agents, Causal World Model, AI for BioScience.

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• [07/01/2026] I am now entering the job market for postdoc opportunities. If my current research interests and past research experience are relevant to your group, please reach out to me!
• [06/11/2025] Delighted to be recognized as an Outstanding Reviewer for KDD 2025 February Cycle.
• [05/15/2025] Our paper on Small Language Model-based Recommendations is accepted by ACL 2025, Stay tuned!
• [12/20/2024] Grateful to receive AAAI-25 Student Scholarship.
• [12/10/2024] Our paper on flexible reflection removal is accepted by AAAI2025.
• [05/01/2023] Join us in the “Trustworthy Recommender Systems” tutorial in The Web Conference 2023. See you in Austin!
• [01/25/2023] Our work on Fairly adaptive negative sampling is accepted by the The Web Conference 2023.

During my PhD study, my research centers on Trustworthy Personalization. I envision a next generation of personalization systems that not only know what users want, but earn their trust while doing so — systems that are transparent in their reasoning, fair and unbiased in their decisions, and faithful to users' genuine intents. Rather than treating accuracy as the sole objective, I aim to build recommenders that people can rely on, so that trust is repeatedly won and continually deserved. My work spans the following directions:

• Fairness: mitigating bias and ensuring equitable treatment across users and item groups, so personalized outcomes do not amplify existing disparities. [WWW'23]
• Reliable Edge Deployment: compressing and adapting personalization models to run efficiently and dependably on resource-constrained edge devices, without sacrificing robustness. [ACL'25] [under review]
• Faithful User Modeling: learning user representations that honestly reflect true preferences and intents, rather than spurious correlations or shortcut signals. [under review]
• Agentic Personalized System: empowering recommenders to act as autonomous agents that plan, reason, and interact to better satisfy user goals, while supporting flexible human-system interaction by understanding diverse forms of user engagement. [AAAI'25]
• Continual Learning for Personalization: enabling systems to learn continuously online once deployed — adapting to evolving interests and non-stationary environments without catastrophic forgetting. [under review]

• [TKDE 2026] A Low-Rank Perspective on Similarity Matrix Completion.
  Changyi Ma, Runsheng Yu, Xiao Chen, Youzhi Zhang, Zhen Lei.
• [ACL 2025 Findings] C2KD: Cross-Layer and Cross-Head Knowledge Distillation for Small Language Model-Based Recommendation.
  Xiao Chen, Changyi Ma, Wenqi Fan, Zhaoxiang Zhang, Qing Li.
• [AAAI 2025] FIRM: Flexible Interactive Reflection Removal.
  Xiao Chen, Xudong Jiang, Yunkang Tao, Zhen Lei, Qing Li, Chenyang Lei, Zhaoxiang Zhang.  [arXiv]
• [WWW 2023] Fairly Adaptive Negative Sampling for Recommendations.
  Xiao Chen, Wenqi Fan, Jingfan Chen, Zhaoxiang Zhang, Qing Li.  [arXiv]
• [arXiv 2023] A Comprehensive Survey on Trustworthy Recommender Systems.
  Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li. [arXiv]

• 2025: Research Intern at Tencent, Shenzhen, working on Agentic Recommender Systems.
• 2022 – Present: Ph.D. in Computer Science at The Hong Kong Polytechnic University, advised by Chair Professor Qing Li and Professor Zhaoxiang Zhang.
• 2020 – 2022: Full-time AI Algorithm Engineer.
• 2017 – 2020: MSc in Computer Science and Technology at Zhejiang University, China.
• 2018 – 2019: Visiting Student at Ritsumeikan University, Japan.
• 2013 – 2017: BSc in Software Engineering at Northwestern Polytechnical University, China.

I write paper-reading notes and literature surveys. Click a title to read the full post.

• Continual Learning Memory Management: A Survey 2026-07-01

  A comprehensive survey of memory management methods in continual learning, organized by techniques: experience replay, generative replay, gradient constraints, dual memory, replay-free approaches, and LLM-Agent methods.

• Recommendation as Generation (RaG): Paper Reading 2026-07-01

Tutorial Co-organizer
Trustworthy Recommender Systems: Foundations and Frontiers — KDD, WWW, IJCAI 2023

Conference & Journal Reviewer
NeurIPS (2024–2026), ICLR (2025–2026), ICML (2025), KDD (2025–2026), ACL ARR (2026), WWW (2025), AAAI (2023, 2026), ECCV (2024), ACM MM (2024, 2026), AISTATS (2025–2026), TOIS (2025), TKDD (2024–2025), TAI (2024)

Student Volunteer
ICDE 2025, ACL 2025

• ⏳ AI Conference Deadlines: Countdowns to top CV/NLP/ML/Robotics/AI conference deadlines.
• 📝 Writing Perfect Papers: A long talk on how to write perfect papers (by Prof. Baochun Li @UofT).
• 🍑 Lilian Weng's Blog: Deep insights for many ML problems.
• 📡 Scientific Spaces: ML|NLP theory blog with deep explorations.

I enjoy hiking, tennis, ukulele, hip-hop, and photography in my spare time.

Thanks for Jon Barron's template