Jiawei Chen
"Hundred Talent" Research Fellow
Researches recommender systems, including generative recommendation, together with large language models and AI agents.
Jiawei Chen's lab at Zhejiang works on recommendation, search & web and large language models. 41 papers since 2023. Applications close .
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 11 papers2026 · T1 0 · collecting
- 9 papers2025 · T1 0
- 9 papers2024 · T1 0
- 12 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Recommendation, search & web24 papers
- recommendation20 papers
- Large language models10 papers
- recommender7 papers
- together0 papers
Paper list
From the lab homepage.
202611 papers
- SIGIR ’26BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models (opens in a new tab)
- WWW ’26Does LLM Focus on the Right Words? Diagnosing Language Bias in LLM-based Recommenders (opens in a new tab)
- WWW ’26Field matters: A lightweight LLM-enhanced method for CTR prediction (opens in a new tab)
- KDD ’26GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies (opens in a new tab)
- ACL ’26ReCreate: Reasoning and Creating Domain Agents Driven by Experience (opens in a new tab)
- ACL ’26Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (opens in a new tab)
- ICLR ’26Scheduling Your LLM Reinforcement Learning with Reasoning Trees (opens in a new tab)
- SIGIR ’26SpecTran: Spectral-Aware Transformer-based Adapter for LLM-Enhanced Sequential Recommendation (opens in a new tab)
- WWW ’26Talos: Optimizing Top-K Accuracy in Recommender Systems (opens in a new tab)
- KDD ’26The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders (opens in a new tab)
- WWW ’26TopKGAT: A Top-K Objective-Driven Architecture for Recommendation (opens in a new tab)
20259 papers
- AAAI ’25Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution (opens in a new tab)
- KDD ’25Breaking the Top-K Barrier: Advancing Top-K Ranking Metrics Optimization in Recommender Systems (opens in a new tab)
- TMLR ’25Conditional Image Synthesis with Diffusion Models: A Survey (opens in a new tab)
- WSDM ’25How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective (opens in a new tab)
- ACM TOIS ’25LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation (opens in a new tab)
- SIGIR ’25MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender (opens in a new tab)
- WWW ’25Rankformer: A graph transformer for recommendation based on ranking objective (opens in a new tab)
- NeurIPS ’25Tree of Preferences for Diversified Recommendation (opens in a new tab)
- WWW ’25Uncertainty-Aware Graph Structure Learning (opens in a new tab)
20249 papers
- ACM CSUR ’24A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions (opens in a new tab)
- ICDE ’24BSL: Understanding and Improving Softmax Loss for Recommendation (opens in a new tab)
- CIKM ’24Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding (opens in a new tab)
- RecSys ’24Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models (opens in a new tab)
- WWW ’24Distributionally Robust Graph-based Recommendation System (opens in a new tab)
- SIGIR ’24EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems (opens in a new tab)
- NeurIPS ’24PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation (opens in a new tab)
- ACM TOIS ’24ReCRec: Reasoning the Causes of Implicit Feedback for Debiased Recommendation (opens in a new tab)
- SIGIR ’24SIGformer: Sign-aware Graph Transformer for Recommendation (opens in a new tab)
202312 papers
- SIGIR ’23A Generic Learning Framework for Sequential Recommendation with Distribution Shifts (opens in a new tab)
- WWW ’23Adap-tau: Adaptively Modulating Embedding Magnitude for Recommendation (opens in a new tab)
- SIGIR ’23Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation (opens in a new tab)
- ACM TOIS ’23Bias and Debias in Recommender System: A Survey and Future Directions (opens in a new tab)
- CIKM ’23CDR: Conservative Doubly Robust Learning for Debiased Recommendation (opens in a new tab)
- ACM TOIS ’23CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System (opens in a new tab)
- IJCAI ’23Discriminative-Invariant Representation Learning for Unbiased Recommendation (opens in a new tab)
- WWW ’23On the Theories Behind Hard Negative Sampling for Recommendation (opens in a new tab)
- NeurIPS ’23OpenGSL: A Comprehensive Benchmark for Graph Structure Learning (opens in a new tab)
- IEEE TKDE ’23Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation (opens in a new tab)
- WSDM ’23Unbiased Knowledge Distillation for Recommendation (opens in a new tab)
- NeurIPS ’23Understanding Contrastive Learning via Distributionally Robust Optimization (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ZhejiangFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- Per ZJU language requirements
- Fee
- RMB 800
Not yet confirmed for this intake.
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