His current research interests include data mining and machine learning, especially

His research has been awarded ICDM'22 and ICDM'21 Best-ranked Papers, Joint AAAI/ACM SIGAI Doctoral Dissertation Award Nomination, Global Chinese AI Rising Stars (Top 25 in Data Mining), CCF-Tencent Open Fund (twice), CCF-Alimama Research Fund, Tencent Focused Research Fund, CCF-Ant Research Fund, CCF-BaiChuan-Ebtech Foundation Model Fund, Ant Group Research Fund, Criteo Faculty Research Award, and MSU Dissertation Fellowship. He is the awardee of the HK RGC Research Impact Fund with a grant of HK$7 million, marking him as the ONLY assistant professor to lead such a key project in Hong Kong since 2020. He is among the top 2% in the Stanford list of the world's most-cited scientists in 2024.

He serves as the top-tier Artificial Intelligence and Data Science conference chair and organizer at WWW'25, ICICIP'25, ISNN'25, WAIM'24, NLPCC'24, and ADC'24, and the journal editor at Neural Networks, ACM Transactions on the Web, and Frontiers in Big Data. He serves as conference area chair and (senior) program committee member over 100 times. He serves as the workshop organizer at KDD'19, SIGIR'20/21/22/24, WWW'21/24, Recsys'23 CIKM'23, and a lead tutor at KDD'23, WWW'21/22/23, IJCAI'21/23 and WSDM'23. He also serves as the founding academic committee member of MLNLP, the largest Chinese AI community with millions of subscribers. The models and algorithms from his research have been launched in the online systems of various companies, such as Amazon, Google, Facebook, Linkedin, Criteo, Lyft, Baidu, Tencent, Ant Group, Kuaishou, and JD.com.

• I have PhD (ddl: HKPFS Dec 1, Normal PhD Dec 31) and Joint-PhD (双一流A类+国科大, ddl: Dec 5, 2024) positions every year
• Postdoc, Self-financed PhD, Part-time PhD, RA and Visiting Students positions are open year round
• Please click HERE for more details (CityU is ranked #10 in QS Asia University Rankings 2025, #3 in HK)

• Medical LLMs, When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications (SIGIR'2024) [link]
• IR LLMs, MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion (NAACL'2024) [link]
• LLMs Theory, Large Multimodal Model Compression via Efficient Pruning and Distillation (WWW'2024, Oral Presentation) [link]
• LLMs Theory, Multi-­perspective Improvement of Knowledge Graph Completion with Large Language Models (COLING'2024) [link]
• Medical LLMs, Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models (CIKM'2024) [link]
• RecSys LLMs, LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation (CIKM'2024) [link]
• RecSys LLMs, Recommender Systems in the Era of Large Language Models (LLMs) (IEEE TKDE) [link]
• LLMs Theory, Large Language Models for Generative Information Extraction: A Survey (Frontiers of Computer Science) [link]
• Medical LLMs, Mitigating Hallucinations of Large Language Models in Medical Domain via Contrastive Decoding (EMNLP'24 Findings) [link]
• RecSys LLMs, A Unified Framework for Multi-Domain CTR Prediction via Large Language Models (ACM TOIS) [link]
• RecSys LLMs, LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation (NeurIPS'24) [link]
• Multi-Modal & Spatial LLMs, G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models (NeurIPS'24) [link]
• Trust LLMs, Seeing is NOT Always Believing! Unveiling the True Symmetric Moral Consistency of Large Language Models (NeurIPS'24) [link]
• RecSys LLMs, Rethinking Large Language Model Architectures for Sequential Recommendations [link]
• Medical LLMs, Large Language Model Distilling Medication Recommendation Model [link]
• IR LLMs, Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM [link]
• RecSys LLMs, Tired of Plugins? Large Language Models Can Be End-To-End Recommenders [link]
• RecSys LLMs, E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation [link]
• RecSys LLMs, LLM-enhanced Reranking in Recommender Systems [link]
• Graph LLMs, LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning [link]

• NeurIPS'2024, Spotlight (3%)
  LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation [link]
  
• NeurIPS'2024, Spotlight (3%)
  Association of Objects May Engender Stereotypes: Mitigating Association-Engendered Stereotypes in Text-to-Image Generation [link]
  
• WWW'2024, Oral Presentation (10%)
  Large Multimodal Model Compression via Efficient Pruning and Distillation [link]
  
• ICDM'2022, Best-ranked Papers Award
  AutoAssign: Automatic Shared Embedding Assignment in Streaming Recommendation [link]
  
• IJCAI'2022, Long Oral Presentation (3.75%)
  MLP4Rec: A Pure MLP Architecture for Sequential Recommendations [link]
  
• WSDM'2021, Top-4 Most Cited Paper 4 / 155 Accepted Papers
  Towards Long-term Fairness in Recommendation [link]
  
• ICDM'2021, Best-ranked Papers Award, Top-10 Most Cited Paper 10 / 208 Accepted Papers
  AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations [link]
  
• PhD Thesis, 2021 Joint AAAI/ACM SIGAI Doctoral Dissertation Award Nomination
  Adaptive and Automated Deep Recommender Systems, extended abstract at ACM SIGWEB Newsletter [link]
  
• ACM SIGWEB Newsletter, Top-2 Most Cited Paper 2 / 551 in History
  Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey [link]
  
• RecSys'2018, Top-1 Most Cited Paper 1 / 117 Accepted Papers
  Deep Reinforcement Learning for Page-wise Recommendations [link]
  
• KDD'2018, Top-20 Most Cited Paper 19 / 294 Accepted Papers
  Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning [link]
  
• CIKM'2017, Top-20 Most Cited Paper 20 / 350 Accepted Papers
  Modeling Temporal-Spatial Correlations for Crime Prediction [link]
  

• Embedding in Recommender Systems: A Survey [link]
• Recommender Systems in the Era of Large Language Models (LLMs) (IEEE TKDE) [link]
• Large Language Models for Generative Information Extraction: A Survey (Frontiers of Computer Science) [link]
• Automated Machine Learning for Deep Recommender Systems: A Survey (ACM TORS) [link]
• Multi-Task Deep Recommender Systems: A Survey [link]
• Multimodal Recommender Systems: A Survey (ACM CSUR) [link]
• A Comprehensive Survey on Trustworthy Recommender Systems [link]
• Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey (ACM SIGWEB Newsletter) [link]
• Mobile Trajectory Anomaly Detection: Taxonomy, Methodology, Challenges, and Directions (IEEE IoT) [link]
• Crime in Urban Areas: A Data Mining Perspective (ACM SIGKDD Explorations) [link]
• On the Opportunities of Green Computing: A Survey [link]

• Tutorial Proposal @ ACM The Web Conference'25 (WWW'25) [Website]
• IEEE ICICIP'25 [Website], ISNN'25 [Website], NLPCC'24 [Website], ADC'24 [Website]