Min Zhang
Professor
Works on information retrieval, personalized recommendation, and user behavior analysis.
Min Zhang's lab at Tsinghua works on recommendation, search & web and knowledge, QA & retrieval. 37 papers since 2023. 31 PhD students and 5 master's students. 41 alumni. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 0%
- PhD students
- 31
- MS students
- 5
- alumni listed
- 41
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 12 papers2025 · T1 0
- 14 papers2024 · T1 0
- 11 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Recommendation, search & web22 papers
- Knowledge, QA & retrieval6 papers
- personalized5 papers
- user behavior0 papers
- behavior analysis0 papers
Paper list
From the lab homepage.
202512 papers
- Min ZhangBeyond Utility: Evaluating LLM as Recommender
- Min ZhangCD-CDR: Conditional Diffusion-based Item Generation for Cross-Domain Recommendation
- Min ZhangExplainable Multi-Modality Alignment for Transferable Recommendation
- Min ZhangGenerative language reconstruction from brain recordings
- Min ZhangImproving Long-tail User CTR Prediction via Hierarchical Distribution Alignment
- Min ZhangIntelligent Agents with Adaptive Knowledge Fusion for Personalized Recommendation
- Min ZhangPerSRV: Personalized Sticker Retrieval with Vision-Language Model
- Min ZhangShort Video Segment-level User Dynamic Interests Modeling in Personalized Recommendation
- Min ZhangSmall Stickers, Big Meanings: A Multilingual Sticker Semantic Understanding Dataset with a Gamified Approach
- Min ZhangStepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
- Min ZhangU-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization
- Min ZhangUser Immersion-aware Short Video Recommendation
202414 papers
- Min ZhangA Situation-aware Enhancer for Personalized Recommendation
- Min ZhangA User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
- Min ZhangAiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
- Min ZhangCollaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
- Min ZhangCommon Sense Enhanced Knowledge-based Recommendation with Large Language Model
- Min ZhangEasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems
- Min ZhangIntersectional Two-sided Fairness in Recommendation
- Min ZhangLarge language models as evaluators for recommendation explanations
- Min ZhangMulti-Agent Collaboration Framework for Recommender Systems
- Min ZhangRelevance Feedback with Brain Signals
- Min ZhangRight Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery
- Min ZhangSequential Recommendation with Latent Relations based on Large Language Model
202311 papers
- Min ZhangBehavior Modeling for Point of Interest Search
- Min ZhangCollaborative word-based pre-trained item representation for transferable recommendation
- Min ZhangIncorporating Structural Information into Legal Case Retrieval
- Min ZhangIntent-aware Ranking Ensemble for Personalized Recommendation
- Min ZhangInvestigating Conversational Agent Action in Legal Case Retrieval
- Min ZhangMeasuring Item Global Residual Value for Fair Recommendation
- Min ZhangSequential Recommendation with Multiple Contrast Signals
- Min ZhangTwo-sided Calibration for Quality-aware Responsible Recommendation
- Min ZhangUnbiased Delayed Feedback Label Correction for Conversion Rate Prediction
- Min ZhangUnderstanding User Immersion in Online Short Video Interaction
- Min ZhangUser Perception of Recommendation Explanation: Are Your Explanations What Users Need?
Where alumni went41 listed
Apply to TsinghuaFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- GRE
- See program page
- English
- Per school catalog
- Fee
- RMB 800
Not yet confirmed for this intake.
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