Mingyue Cheng (程明月)  

Ph.D, Associate Researcher

State Key Laboratory of Cognitive Intelligence
School of Computer Science and Technology, University of Science and Technology of China

Research Group: USTC-AGI Research Group

Email: mycheng@ustc.edu.cn  
Add: B710, Xinzhi Building, Gaoxin Campus of USTC, Hefei, Anhui, China, 230026

Biography

I am an Associate Reseacher at the School of Computer Science and Technology, USTC. I am also affiliated with the State Key Laboratory of Cognitive Intelligence at the side of USTC, working under the leadership of Prof. Enhong Chen and Prof. Qi Liu. Previously, I obtained my Ph.D. degree under the supervision of Prof. Qi Liu .

Research Interests

My primary research interests lie in data mining and machine learning algorithms for structured data, including sequence and tabular data. I am deeply committed to developing AI systems that are not only powerful—both effective and efficient—but also trustworthy, with a focus on privacy awareness and explainability. Specifically, my interests include the following topics:


欢迎脚踏实地而又积极主动的本科生、研究生同学加入实验室USTC-AGI研究组。同时，欢迎大家体验我们精心打造的冰鉴大模型评测平台，共同助力大模型诊断评测；以及使用我们团队精心自研的文修智能助写平台提升写作效率与技能。

Working Experiences

Education

Selected Publications (Google Scholar) (Github)

(* Corresponding Author, + Equal Contribution)

  1. Mingyue Cheng, Jiqian Yang, Tingyue Pan, Qi Liu*, Zhi Li, ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis. (Preprint) [PDF] [Code]
  2. Xiaoyu Tao, Tingyue Pan, Mingyue Cheng*, Yucong Luo HiTime: Hierarchical Multimodal LLMs with Semantic Space Alignmentfor Enhanced Time Series Classification. (Preprint) [PDF] [Code]
  3. Mingyue Cheng, Qi Liu*, Zhiding Liu, Hao Zhang, Rujiao Zhang, Enhong Chen, TimeMAE: Self-supervised Representation of Time Series with Decoupled Masked Autoencoders. (Preprint) [PDF] [Code]
  4. Daoyu Wang, Mingyue Cheng*, Zhiding Liu, Qi Liu, Enhong Chen, Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting. (Preprint) [PDF] [Code]
  5. Jintao Zhang, Mingyue Cheng*, Xiaoyu Tao, Zhiding Liu, Daoyu Wang, FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial Modeling. (Preprint) [PDF] [Code]
  6. Mingyue Cheng, Jintao Zhang, Zhiding Liu, Chunli Liu*, Yanhu Xie, HMF: A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction. (Preprint) [PDF] [Code]
  7. Shuo Yu, Mingyue Cheng*, Jiqian Yang, Jie Ouyang, A Knowledge-Centric Benchmarking Framework and Empirical Study for Retrieval-Augmented Generation. (Preprint)[PDF] [Code]
  8. Qitao Qin, Yucong Luo, Mingyue Cheng*, Qingyang Mao, Chenyi Lei, TDV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation. (Preprint) [PDF] [Code]
  9. Hao Zhang, Mingyue Cheng*, Qi Liu, Zhiding Liu, Linbo Zhu, Yu Su, Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems. (Preprint)[PDF] [Code]
  10. Mingyue Cheng, Yiheng Chen, Qi Liu*, Zhiding Liu, Yucong Luo, Enhong Chen InstructTime: Advancing Time Series Classification with Multimodal Language Modeling, The 18th ACM International Conference on Web Search and Data Mining (Accepted, ACM WSDM2025) [PDF] [Code] [Poster]
  11. Mingyue Cheng, Xiaoyu Tao, Qi Liu*, Hao Zhang, Yiheng Chen, Defu Lian, CrossTimeNet: Cross-Domain Pre-training with Language Models for Transferable Time Series Representations, The 18th ACM International Conference on Web Search and Data Mining (Accepted, ACM WSDM2025) [PDF] [Code][Poster]
  12. Jie Ouyang, Yucong Luo, Mingyue Cheng*, Shuo Yu, Daoyu Wang, Qi Liu, Enhong Chen, Revisiting the Solution of Meta KDD Cup 2024: CRAG. (Accepted, 2024 KDD Cup Workshop for Retrieval Augmented Generation, (Second Place in Task 2 & Task 3 of KDD Cup 2024)) [Code][Slides][Poster].
  13. Rujiao Zhang, Hao Zhang, Yucong Luo, Zhiding Liu, Mingyue Cheng, Qi Liu, Enhong Chen*, Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information, The 33rd ACM International Conference on Information and Knowledge Management (ACM CIKM2024):4288 - 4292, October 21–25, 2024, Boise, ID, USA. [Code][Slides]
  14. Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Zhijin Wang, Bo Hu, Zang Li, TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback, The 8th APWeb-WAIM Joint International Conference on Web and Big Data（APWeb-WAIM2024): 193-208, August 30 - September 1, Jinhua, China. [PDF] [Poster]
  15. Zhiding Liu, Jiqian Yang, Mingyue Cheng*, Yucong Luo, Zhi Li, Generative Pretrained Hierarchical Transformer for Time Series Forecasting(>ACM SIGKDD2024):2003-2013, August 25-29, 2024, Barcelona Spain, 2024. [PDF] [Code]
  16. Yucong Luo, Mingyue Cheng, Hao Zhang, Junyu Lu, Enhong Chen*, Unlocking the Potential of Large Language Models for Explainable Recommendations. (DASFAA2024)[PDF][Code]
  17. Junzhe Jiang, Shang Qu, Mingyue Cheng*, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, Kai Zhang, Rui Li, Jiatong Li, Min Gao, Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling. (Accepted, DASFAA2024)[PDF][Code]
  18. Mingyue Cheng, Hao Zhang, Qi Liu*, Fajie Yuan, Zhi Li, Zhenya Huang, Enhong Chen, Longfei Li, Jun Zhou, Empowering Sequential Recommender Systems from Mixture of Collaborative Signals and Semantic Relatedness. (Accepted, DASFAA2024)[PDF][Code]
  19. Hao Zhang, Mingyue Cheng*, Qi Liu, Yucong Luo, Rui Li, Enhong Chen, Learning Recommender Systems with Soft Target: A Decoupled Perspective (Accepted, DASFAA2024). [PDF][Code][Poster]
  20. Mingyue Cheng, Hao Zhang, Jiqian Yang, Qi Liu*, Li Li, Xin Huang, Liwei Song, Zhi Li, Zhenya Huang, Enhong Chen, Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform. (WWW2024, Accepted)
  21. Junchen Fu, Fajie Yuan, Yu Song, Zheng Yuan, Mingyue Cheng, Shenghui Cheng, Jiaqi Zhang, Jie Wang, Yunzhu Pan, Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights, The 17th ACM International Conference on Web Search and Data Mining (WSDM'2024): 208-217, Mar 4-8, 2024, Mérida, México. [PDF] [Code]
  22. Zhiding Liu, Mingyue Cheng, Zhi Li, Zhenya Huang, Qi Liu, Enhong Chen, Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective, The 37th Advances in Neural Information Processing Systems (NeurIPS'2023):36, Dec 9-15, 2023, New Orleans, Louisiana, USA. [PDF] [Code]
  23. 耿杰, 刘春丽*, 魏雪梅, 程明月, 袁昆, 李洋, 刘业政, 基于用户重购行为的产品推荐方法, 计算机研究与发展 2023, 60(8): 1795-1807
  24. Mingyue Cheng, Qi Liu*, Wenyu Zhang, Zhiding Liu, Hongke Zhao, Enhong Chen, A General Tail Item Representation Enhancement Framework for Sequential Recommender Systems, Frontier of Comupter Science (FCS), 2024, 18(6): 1-12. [PDF] [Code]
  25. Mingyue Cheng, Qi Liu*, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen, FormerTime: Hierarchical Multi-scale Representation for Multivariate Time Series Classification, The 32nd International World Wide Web Conference (ACM WWW'2023): 1437–1445, Austin, TX, USA. [PDF] [Code]
  26. Wenqiang He, Mingyue Cheng, Qi Liu*, Zhi Li, ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification, The 28th International Conference on Database Systems for Advanced Applications (DASFAA'2023): 353–369, Tianjin, China.
  27. Mingyue Cheng, Zhiding Liu+, Qi Liu*, Shenyang Ge, Enhong Chen, Towards Automatic Designing of Deep Hybrid Network Architecture for Sequential Recommendation, The 31st International World Wide Web Conference (ACM WWW'2022):1923-1932, Virtual Conference. [Code]
  28. Zhiding Liu, Mingyue Cheng, Zhi Li, Qi Liu, Enhong Chen*, One Person, One Model—Learning Compound Router for Sequential Recommendation, The 22nd IEEE International Conference on Data Mining (IEEE ICDM'2022).[Code]
  29. Junzhe Jiang, Mingyue Cheng, Qi Liu*, Zhi Li, Enhong Chen, Nested Named Entity Recognition from Medical Texts: A Multi-task Learning Approach, The Second CAAI International Conference on Artificial Intelligence (CAAI CICAI'2022):248-259, Bejing, China.
  30. Runlong Yu, Qi Liu*, Yuyang Ye, Mingyue Cheng, Enhong Chen, Jianhui Ma, Collaborative List-and-Pairwise Filtering from Implicit Feedback, IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE'2022): 34(6): 2667-2680, June 2022.
  31. Kai Zhang, Qi Liu*, Zhenya Huang, Mingyue Cheng, Kun Zhang, Mengdi Zhang, Wei Wu, Enhong Chen, Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification, The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR'2022): 1566-1576, Virtual Conference.
  32. Mingyue Cheng, Fajie Yuan+, Qi Liu*, Xin Xin, Enhong Chen, Learning Transferrable User Representations with Sequential Behaviors via Contrastive Pre-training, The 21st IEEE International Conference on Data Mining (IEEE ICDM'2021):51-60, Auckland, New Zealand, December 7-10, 2021.
  33. Mingyue Cheng, Fajie Yuan+, Qi Liu*, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan, Enhong Chen, Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement, The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval ( ACM SIGIR'2021): 575-584, July 11–15, 2021, Virtual Event, Canada.
  34. Linan Yue, Qi Liu*, Han Wu, Kai Zhang, Yanqing An, Mingyue Cheng, Biao Yin, Dayong Wu, NeurJudge: A Circumstance-aware Neural Framework for Legal Judgment Prediction, The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval ( ACM SIGIR'2021):973–982, July 11–15, 2021, Virtual Event, Canada.
  35. Yanqing An, Qi Liu*, Han Wu, Kai Zhang, Linan Yue, Mingyue Cheng, Hongke Zhao, Senchao Yuan, Enhong Chen, LawyerPAN: A Proficiency Assessment Network for Trial Lawyers, The 27th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD'2021):5-13, August 14–18, 2021, Virtual Event, Singapore.
  36. Mingyue Cheng, Runlong Yu, Qi Liu*, Hongke Zhao, Hefu Zhang, Enhong Chen, Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering, The 19th IEEE International Conference on Data Mining (IEEE ICDM'2019): 1000-1005, Beijing, China.
  37. 程明月, 刘淇*, 李徵, 于润龙, 高维博, 陈恩红, 多重对级贝叶斯个性化排序算法. (南京信息工程大学学报自然科学版: 2019年03期302-308)

Professional Activities

Honors and Awards

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