Jiawei Chen

Jiawei Chen is a "Hundred Talent" Research Fellow at Zhejiang University's College of Computer Science and Technology, with over 60 publications in top-tier venues including WWW, SIGIR, KDD, and NeurIPS. His research focuses on advancing recommender systems through innovative approaches to debiasing, graph-based learning, and large language model integration.

His educational background includes:

• Ph.D. in Computer Science, Zhejiang University (2017-2020)
• Master's in Computer Science, Zhejiang University (2014-2017)
• Bachelor's in Micro-electronic, University of Electronic Science and Technology of China (2010-2014)

Chen's research centers on solving fundamental challenges in recommender systems, particularly addressing popularity bias through spectral analysis and causal inference. His work bridges graph mining, knowledge representation, and large language models to develop robust recommendation frameworks. Recent contributions include pioneering graph transformers for ranking optimization and counterfactual reasoning to burst filter bubbles. His influential surveys on recommendation debiasing and deep clustering have established foundational taxonomies for the field.

Analysis of his 2023-2025 publications reveals three dominant trends: (1) Deepening causal approaches to bias mitigation through counterfactual interventions and distributionally robust optimization, (2) Advancing graph-based architectures with sign-aware transformers and uncertainty-aware structure learning, and (3) Integrating large language models through knowledge distillation techniques to enhance sequential recommendation. His work consistently targets high-impact solutions to popularity bias while maintaining strong theoretical grounding.

His research excellence has been recognized with:

• Best Paper Award at WSDM 2025 for spectral analysis of popularity bias amplification
• Best Paper Honorable Mention at SIGIR 2023 for offline reinforcement learning in recommendation

Chen actively mentors future researchers and seeks self-motivated graduate students for projects in recommendation systems, LLMs, and graph mining. His extensive service as program committee member for WWW, AAAI, KDD, and SIGIR—along with reviewing for IEEE TNNLS, TKDE, and TOIS—demonstrates significant community leadership. He has released valuable community resources including the KuaiRec and KuaiRand datasets for unbiased recommendation research.

While specific lab affiliations aren't detailed, Chen maintains strong collaborative ties with researchers across institutions, particularly with Prof. Xiangnan He's group. His work frequently involves large-scale industrial datasets and open-source tools like EasyRL4Rec, indicating leadership in practical recommendation system development and community resource sharing.