Chao Wang 王超

中国科学技术大学人工智能与数据科学学院, 预聘副教授
Email: wangchaoai@ustc.edu.cn

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王超，现为中国科学技术大学人工智能与数据科学学院预聘副教授。2016年于中国科学技术大学少年班学院获理科学士学位，2022年于中国科学技术大学计算机学院获工学博士学位，博士导师为熊辉教授和陈恩红教授，2024年于广州市香港科大霍英东研究院、香港科技大学（广州）博士后出站，博士后合作导师为王炜教授。

主要研究方向为数据挖掘、大模型应用、图神经网络、推荐系统等领域。近年来，主持国家博士后面上基金1项、广州市博士后科研项目1项、南沙区博士后科研项目1项，作为项目骨干参与科技部重点研发项目1项。在相关领域国际重要期刊及会议发表论文30余篇，其中以第一作者及通讯作者身份发表CCF推荐的A类期刊和会议论文10余篇，已公开专利7项，与讯飞、百度等企业长期合作，参与制定中国人工智能产业发展联盟团体标准1项。曾获得中国计算机学会CCF优秀博士学位论文激励计划（CCF优博，每年仅10人）、《中国科学：信息科学》2022年度热点论文（每年4篇）、中科院院长优秀奖等荣誉。

Chao Wang is currently an Assistant Professor at the School of Artificial Intelligence and Data Science, University of Science and Technology of China (USTC). He received his Bachelor of Science degree from the School of the Gifted Young, USTC, in 2016, and his Ph.D. in Engineering from the School of Computer Science, USTC, in 2022, under the supervision of Professors Hui Xiong and Enhong Chen. In 2024, he completed his postdoctoral research at the Fok Ying Tung Research Institute, Hong Kong University of Science and Technology (Guangzhou), with Professor Wei Wang as his postdoctoral advisor.

His main research areas include data mining, large model applications, graph neural networks, and recommendation systems. In recent years, he has led one National Postdoctoral Science Foundation project, one Guangzhou Postdoctoral Research project, and one Nansha District Postdoctoral Research project, and has served as a key researcher in a Ministry of Science and Technology Key R&D project. He has published over 30 papers in internationally recognized journals and conferences, with more than 10 papers in CCF-recommended A-level journals and conferences as the first or corresponding author. He has also filed seven patents. He has long-term collaborations with companies such as iFlytek and Baidu, and has contributed to the development of a group standard for the Chinese AI Industry Development Alliance. His honors include the CCF Excellent Doctoral Dissertation Award (awarded to only 10 individuals annually), the 2022 Hot Paper Award from Science China: Information Sciences (awarded to only 4 papers annually), and the President’s Award from the Chinese Academy of Sciences.


代表性论著

  1. Leilei Ding, Zhipeng Tang, Le Zhang, Dazhong Shen, Chao Wang, Ziyang Tao, Jingbo Zhou, Yanyong Zhang, Hui Xiong. Killing two birds with one stone: A Spatio-Temporal Prompt for the Inductive Spatio-Temporal Extrapolation. The 30th International Conference on Database Systems for Advanced Applications (DASFAA-2025), 2025, accepted. (CCF B)

  2. Shengzhe Zhang, Liyi Chen, Dazhong Shen, Chao Wang*, Hui Xiong*. Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation. Proceedings of the 32nd World Wide Web Conference (WWW-2025), 2025, accepted. (CCF A)

  3. Haoran Xin, Ying Sun, Chao Wang, Hui Xiong. LLMCDSR: Enhancing Cross-Domain Sequential Recommendation with Large Language Models. ACM Transactions on Information Systems (ACM TOIS), 2025, accepted. (CCF A)

  4. Xi Chen, Chuan Qin, Chuyu Fang, Chao Wang, Chen Zhu, Fuzhen Zhuang, Hengshu Zhu, Hui Xiong. Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking. The Thirty-eight Conference on Neural Information Processing Systems, (NeurIPS-2024), 2024, accepted. (CCF A)

  5. Tianfu Wang, Liwei Deng, Chao Wang, Jianxun Lian, Yue Yan, Nicholas Jing Yuan, Qi Zhang, Hui Xiong. COMET: NFT Price Prediction with Wallet Profiling. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (KDD-2024), 2024, accepted. (CCF A)

  6. Leilei Ding, Dazhong Shen, Chao Wang*, Tianfu Wang, Le Zhang, Yanyong Zhang*. DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives. The 33rd International Joint Conference on Artificial Intelligence, (IJCAI-2024), 2024, accepted. (CCF A)

  7. Tianfu Wang, Qilin Fan, Chao Wang*, Long Yang, Leilei Ding, Nicholas Jing Yuan, Hui Xiong*. FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation. The 33rd International Joint Conference on Artificial Intelligence, (IJCAI-2024), 2024, accepted. (CCF A)

  8. Xi Chen, Chuan Qin, Zhigaoyuan Wang, Yihang Cheng, Chao Wang, Hengshu Zhu, Hui Xiong. Pre-DyGAE: Pre-training Enhanced Dynamic Graph Autoencoder for Occupational Skill Demand Forecasting. The 33rd International Joint Conference on Artificial Intelligence, (IJCAI-2024), 2024, accepted. (CCF A)

  9. Wei Wu, Chao Wang*, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong*. AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations. The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR-2024), 2024, accepted. (CCF A)

  10. Yunqin Zhu, Chao Wang*, Qi Zhang, Hui Xiong*. Graph Signal Diffusion Model for Collaborative Filtering. The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR-2024), 2024, accepted. (CCF A)

  11. Shuyao Wang, Yongduo Sui, Chao Wang, Hui Xiong. Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning. Proceedings of the 31st World Wide Web Conference (WWW-2024), 2024. (CCF A)

  12. Shengzhe Zhang, Liyi Chen, Chao Wang, Shuangli Li, Hui Xiong. Temporal Graph Contrastive Learning for Sequential Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2024), 2024, accepted. (CCF A)

  13. Shasha Hu, Chao Wang*, Chuan Qin, Hengshu Zhu, and Hui Xiong*. Super-node Generation for GNN-based Recommender Systems: Enhancing Distant Node Integration via Graph Coarsening. The 29th International Conference on Database Systems for Advanced Applications (DASFAA-2024), Gifu, Japan, accepted, 2024. (CCF B)

  14. Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui Xiong. SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System. ACM Transactions on Information Systems (ACM TOIS), 2023, accepted. (CCF A)

  15. Siyuan Hao, Le Dai, Le Zhang, Shengming Zhang, Chao Wang, Chuan Qin, and Hui Xiong. Hybrid Heterogeneous Graph Neural Networks for Fund Performance Prediction. In Proceedings of the 16th International Conference on Knowledge Science, Engineering and Management (KSEM-2023), accepted, Guangzhou, China, 2023. (CCF C)

  16. Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Xiangyu Zhao, Baoxing Huai, Xian Wu, and Enhong Chen. Interaction-aware drug package recommendation via policy gradient. ACM Transactions on Information Systems (ACM TOIS), 2022, 41(1): 1-32. (CCF A)

  17. Chao Wang, Hengshu Zhu, Peng Wang, Chen Zhu, Xi Zhang, Enhong Chen, Hui Xiong. Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach. ACM Transactions on Information Systems (ACM TOIS), 2022, 40(4): 1-32. (CCF A)

  18. Chao Wang, Hengshu Zhu, Qiming Hao, Keli Xiao, Hui Xiong. Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective. In Proceedings of the 28th World Wide Web Conference (WWW-2021), Ljubljana, 2021. (CCF A)

  19. Dazhong Shen, Chuan Qin, Chao Wang, Zheng Dong, Hengshu Zhu, and Hui Xiong. Topic Modeling Revisited: A Document Graph-based Neural Network Perspective. The 35th Conference on Neural Information Processing Systems (NeurIPS-2021), Virtual Conference, Dec. 6-14th 2021. (CCF A)

  20. Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Baoxing Huai, Tongzhu Liu, Enhong Chen. Drug Package Recommendation via Interaction-aware Graph Induction. The 30th International World Wide Web Conference (WWW-2021), Ljubljana, Slovenia, April 19-23 2021. (CCF A)

  21. Dazhong Shen, Chuan Qin, Chao Wang, Hengshu Zhu, Enhong Chen, Hui Xiong. Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness. In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-2021), 2021. (CCF A)

  22. Miao Chen, Chao Wang, Chuan Qin, Tong Xu, Jianhui Ma, Enhong Chen, Hui Xiong. A Trend-aware Investment Target Recommendation System with Heterogeneous Graph. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN-2021), Shenzhen, China, 2021. (CCF C)

  23. Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui Xiong. SetRank: A setwise Bayesian approach for collaborative ranking from implicit feedback. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2020). 2020, 34(04): 6127-6136. (CCF A)

  24. Chao Wang, Hengshu Zhu, Chen Zhu, Xi Zhang, Enhong Chen, Hui Xiong. Personalized Employee Training Course Recommendation with Career Development Awareness. Proceedings of The Web Conference 2020 (WWW-2020). 2020: 1648-1659. (CCF A)

  25. Chuan Qin, Hengshu Zhu, Fuzhen Zhuang, Qingyu Guo, Qi Zhang, Le Zhang, Chao Wang, Enhong Chen, Hui Xiong. A survey on knowledge graph-based recommender systems. Scientia Sinica Informationis, 2020, 50(7): 937-956. [秦川, 祝恒书, 庄福振, 郭庆宇, 张琦, 张乐, 王超, 陈恩红, 熊辉, 基于知识图谱的推荐系统研究综述, 中国科学: 信息科学, 2020] (CCF A 中文核心)

  26. Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He. Molecular property prediction: A multilevel quantum interactions modeling perspective. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2019). 2019, 33(01): 1052-1060. (CCF A)

  27. Xiaoqing Huang, Qi Liu, Chao Wang, Haoyu Han, Jianhui Ma, Enhong Chen. Constructing Educational Concept Maps with Multiple Relationships from Multi-source Data. 2019 IEEE International Conference on Data Mining (ICDM-2019). IEEE, 2019: 1108-1113. (CCF B)

  28. Chao Wang, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, Zhenya Huang. Confidence-aware matrix factorization for recommender systems. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2018). 2018, 32(1). (CCF A)

  29. Runlong Yu, Yunzhou Zhang, Yuyang Ye, Le Wu, Chao Wang, Qi Liu, Enhong Chen. Multiple pairwise ranking with implicit feedback. Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM-2018). 2018: 1727-1730. (CCF B)

  30. Chao Wang, Qi Liu, Enhong Chen, Zhenya Huang, Tianyu Zhu, Yu Su, Guoping Hu. The Rapid Calculation Method of DINA Model for Large Scale Cognitive Diagnosis. [王超，刘淇，陈恩红，黄振亚，朱天宇，苏喻，胡国平，面向大规模认知诊断的DINA模型快速计算方法研究, 电子学报，46(5):1047-1055，2018]. (CCF A 中文核心)