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Fuzhen Zhuang (庄福振)

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I am currently a full Professor in Institute of Artificial Intelligence, Beihang University.

I am the Director of Research Center for Intelligent Perception and Cognition (智能感知与认知研究中心主任), Institute of Artificial Intelligence, Beihang University.

My research interests include Machine Learning and Data Mining, including Transfer Learning, Multi-task Learning, Multi-view Learning, Recommendation Systems and Knowledge Graph.

I have published more than 150 papers in the prestigious refereed conferences and journals, such as KDD, WWW, SIGIR, ICDE, IJCAI, AAAI, EMNLP, Nature Communications, IEEE TKDE, ACM TKDD, IEEE T-CYB, IEEE TNNLS, ACM TIST, etc.

Address: XueYuan Road No.37, HaiDian District, BeiJing, China.

Homepage of Our Research Group: https://ktl.buaa.edu.cn/.

Email: zhuangfuzhen@buaa.edu.cn & zfz20081983@gmail.com

  • Motto: If you want to harvest, please try your best! Keep your heart passional, everything is possible!

  • I am always seeking for the high intelligent and diligent intern students! If you are interested in my group, please contact me by email zhuangfuzhen@buaa.edu.cn.

    • The new book "Transfer Learning Algorithms: Applications and Practices" (迁移学习算法：应用与实践) has launched.

    书籍：迁移学习算法：应用与实践。正式出版，2023。

    News

    • I am invited to serve as the Area Chair of KDD 2025 (Aug Cycle).

    • I am invited to serve as the Area Chair of AAAI 2025.

    • I serve as the Area Chair of KDD 2024, AAAI 2024, the Senior PC of SDM 2024 and PAKDD 2024.

    • Two papers have been accepted by NeurIPS 2024.

      • 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. NeurIPS 2024.

      • Mingyi Li, Xiao Zhang, Qi Wang, Tengfei LIU, Ruofan Wu, Weiqiang Wang, Fuzhen Zhuang, Hui Xiong, Dongxiao Yu.: Resource-Aware Federated Self-Supervised Learning with Global Class Representations. NeurIPS 2024.

    • One paper has been accepted by ACM MM 2024.

      • Yuting Zhang, Zhao Zhang, Yiqing Wu, Ying Sun, Fuzhen Zhuang*, Wenhui Yu, Lantao Hu, Han Li, Kun Gai, Zhulin An, Yongjun Xu.: Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video Recommendation. ACM MM 2024.

    • Two papers have been accepted by TOIS 2024.

      • Ying Sun, Yang Ji, Hengshu Zhu, Fuzhen Zhuang, Qing He, Hui Xiong.: Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning. TOIS 2024.

      • Yiheng Jiang, Yuanbo Xu, Yongjian Yang, Funing Yang, Pengyang Wang, Chaozhuo Li, Fuzhen Zhuang, Hui Xiong.: TriMLP: A Foundational MLP-like Architecture for Sequential Recommendation. TOIS 2024.

    • Four papers have been accepted by TKDE 2024.

      • Yuanbo Xu, Fuzhen Zhuang, En Wang, Chaozhuo Li, Jie Wu.: Learning without Missing-At-Random Prior Propensity-A Generative Approach for Recommender Systems. TKDE 2024.

      • Xiao Zhang, Shuqing Xu, Huashan Chen, Zekai Chen, Fuzhen Zhuang, Hui Xiong, Dongxiao Yu.: Rethinking Robust Multivariate Time Series Anomaly Detection: A Hierarchical Spatio-Temporal Variational Perspective. TKDE 2024.

      • Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang*, Yu Zhao, Deqing Wang, Hongwei Zheng.: Temporal Knowledge Graph Reasoning with Dynamic Memory Enhancement. TKDE 2024.

      • Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang∗, Xu Zhang, Leyu Lin, Qing He.: Personalized Prompt for Sequential Recommendation. TKDE 2024.

    • Four papers have been accepted by KDD 2024.

      • Yuting Zhang, Yiqing Wu, ruidong han, Ying Sun, Yongchun Zhu, Xiang Li, Wei Lin, Fuzhen Zhuang*, Zhulin An, Yongjun Xu.: Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation. KDD 2024.

      • Yanjie Gou, Yuanzhou Yao, Zhao Zhang, Yiqing Wu, Yi Hu, Fuzhen Zhuang, Jiangming Liu, Yongjun Xu.: Controllable Multi-Behavior Recommendation for In-Game Skins with Large Sequential Model. KDD 2024.

      • Yiqing Wu, Ruobing Xie, Zhao Zhang, Xuonezhang, Fuzhen Zhuang, Leyu Lin, Zhanhui Kang, Yongjun Xu.: Dual-frequency Graph Neural Network for Sign-aware Recommendation. KDD 2024.

      • Huaming Du, Long Shi, Xingyan Chen, Yu Zhao, Hegui Zhang, Carl Yang, Fuzhen Zhuang, Gang Kou.: Representation Learning of Temporal Graphs with Structural Roles. KDD 2024.

    • One paper has been accepted by AAAI 2024.

      • Wei Chen, YuXuan Liu, Zhao Zhang, Fuzhen Zhuang*, Jiang Zhong.: Modeling Adaptive Inter-Task Feature Interactions via Sentiment-Aware Contrastive Learning for Joint Aspect-Sentiment Prediction. AAAI 2024.

    • Two papers have been accepted by TOIS 2023.

      • Wei Chen, Yiqing Wu, Zhao Zhang, Fuzhen Zhuang*, Zhongshi He, Ruobin Xie, Feng Xia.: FairGap: Fairness-aware Recommendation via Generating Counterfactual Graph. TOIS 2023.

      • Yuting Zhang, Ying Sun, Fuzhen Zhuang*, Yongchun Zhu, Zhulin An, Yongjun Xu.: Triple Dual Learning for Opinion-Based Explainable Recommendation. TOIS 2023.

    • Two paper has been accepted by ICDE 2024.

      • Yongjing Hao, Pengpeng Zhao, Junhua Fang, Jianfeng Qu, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou.: Meta-optimized Structural and Semantic Contrastive Learning for Graph Collaborative Filtering. ICDE 2024.

      • Yongjing Hao, Pengpeng Zhao, Junhua Fang, Jianfeng Qu, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou.: Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation. ICDE 2024.

    • Our paper has been selected as Best Short Paper in CIKM 2023.

      • Zhao Zhang, Fuwei Zhang, Fuzhen Zhuang*, Yongjun Xu.: Knowledge Graph Error Detection with Hierarchical Path Structure. CIKM 2023.

    • One paper has been accepted by TKDE 2023.

      • Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang.: Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction. TKDE 2023.

    • Two papers have been accepted by KDD 2023.

      • Yuting Zhang, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang*, Ruidong Han, Xiang Li, Wei Lin, Zhulin An, Yongjun Xu.: Modeling Dual Period-Varying Preferences for Takeaway Recommendation. KDD 2023. (ADS Track)

      • Chuyu Fang, Chuan Qin, Qi Zhang, Kaichun Yao, Jingshuai Zhang, Hengshu Zhu, Fuzhen Zhuang*, Hui Xiong.: RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment. KDD 2023. (ADS Track)

    • Eight papers have been accepted by SIGIR 2023.

      • Zhao Zhang, Zhanpeng Guan, Fuwei Zhang, Fuzhen Zhuang*, Zhulin An, Fei Wang, Yongjun Xu.: Weighted Knowledge Graph Embedding. SIGIR 2023. (Full paper)

      • Chenguang Du, Kaichun Yao, Hengshu Zhu, Deqing Wang, Fuzhen Zhuang, Hui Xiong.: Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph. SIGIR 2023. (Full paper)

      • Xiao Zhang, Ziming Ye, Jianfeng Lu, Fuzhen Zhuang, Yanwei Zheng, Dongxiao Yu.: Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity. SIGIR 2023. (Full paper)

      • Hanwen Du, Huanhuan Yuan, Pengpeng Zhao, Fuzhen Zhuang, Guanfeng Liu, Lei Zhao, Yanchi Liu, Victor S. Sheng.: Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation. SIGIR 2023. (Full paper)

      • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, Victor Sheng.: Meta-optimized Contrastive Learning for Sequential Recommendation. SIGIR 2023. (Full paper)

      • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Yanchi Liu, Victor S Sheng.: Frequency Enhanced Hybrid Attention Network for Sequential Recommendation. SIGIR 2023. (Full paper)

      • Shuokai Li, Jingbo Zhou, Jizhou Huang, Hao Chen, Fuzhen Zhuang, Qing H, Dejing Dou.: Matching Point of Interests and Travel Blog with Multi-view Information Fusion. SIGIR 2023. (Short paper)

      • Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie Zhou, Yongjun Xu, Qing He.: Attacking Pre-trained Recommendation. SIGIR 2023. (Short paper)

    • One paper has been accepted by ACL 2023.

      • Ting Jiang, Deqing wang, Fuzhen Zhuang, Ruobing Xie, Feng Xia.: Pruning Pre-trained Language Models Without Fine-Tuning. ACL 2023.

    • One paper has been accepted by TPAMI 2023.

      • Chuanguang Yang, Zhulin An, Helong Zhou, Fuzhen Zhuang, Yongjun Xu, Qian Zhang.: Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition. TPAMI 2023.

    • Two papers have been accepted by TheWebConf 2023.

      • Yuxin Ying, Fuzhen Zhuang, Yongchun Zhu, Deqing Wang, Hongwei Zheng.: CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation. TheWebConf 2023.

      • Yuanzhou Yao, Zhao Zhang, Kaijia Yang, Huasheng Liang, Qiang Yan, Fuzheng Zhuang, Boyu Diao, Yongjun Xu, Chao Li.: A Knowledge Enhanced Hierarchical Fusion Network for CTR Prediction under Account Search Senario in WeChat. TheWebConf 2023 (Industry Track).

    • One paper has been accepted by TOIS 2022.

      • Qiming Li, Zhao Zhang, Fuzhen Zhuang, Yongjun Xu, and Chao Li. Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement. TOIS 2022.

    • One paper has been accepted by KDD 2022.

      • Xiexiong Lin, Huaisong Li, Tao Huang, Feng Wang, Taifeng Wang, Tianyi Zhang, Fuzhen Zhuang, Linlin Chao.: A Logic Aware Neural Generation Method for Explainable Data-to-text. KDD 2022. (ADS Track)

    • One paper has been accepted by IJCAI 2022.

      • Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang.: Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion. IJCAI 2022.

    • Three papers have been accepted by SIGIR 2022.

      • Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, Qing He.: User-Centric Conversational Recommendation with Multi-Aspect User Modeling. SIGIR 2022. (Long)

      • Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, Fuzhen Zhuang.: Generalizing to the Future: Mitigating Entity Bias in Fake News Detection. SIGIR 2022. (Short)

      • Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He.: Selective Fairness in Recommendation via Prompts. SIGIR 2022. (Short)

    • One paper has been accepted by TheWebConf 2022.

      • Zhendong Chen, Siu Cheung Hui, Lejian Liao, Fuzhen Zhuang, Fei Li, Meihuizi Jia, Jiaqi Li.: EvidenceNet: Evidence Fusion Network for Fact Verification. TheWebConf 2022.

    • Five papers have been accepted by TKDE 2022.

      • Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Kang Kou.: Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks. TKDE 2022.

      • Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Gang Kou.: Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks. TKDE 2022.

      • Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, Fuzhen Zhuang.: Memory-Guided Multi-View Multi-Domain Fake News Detection. TKDE 2022.

      • Yuyang Xu, Haochao Ying, Siyi Qian, Fuzhen Zhuang, Xiao Zhang, Deqing Wang, Jian Wu, Hui Xiong.: Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction. TKDE 2022.

      • Yu Zhao, Han Zhou, Anxiang Zhang, Ruobing Xie, Qing Li, Fuzhen Zhuang*.: Connecting Embeddings Based on Multiplex Relational Graph Attention Networks for Knowledge Graph Entity Typing. TKDE 2022.

    • One paper has been accepted by AAAI 2022.

      • Fuwei Zhang, Zhao Zhang, Xiang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei, Qing He: Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement. AAAI 2022.

    Research Interests

    • Machine Learning

    • Data Mining

    • Transfer Learning

    • Multi-task Learning

    • Multi-view Learning

    • Recommender Systems.

    • Knowledge Graph.

    Professional Experience

    • Professor. Institute of Artificial Intelligence, Beihang University, Apr 2021 - Present.

    • Associate Professor. Institute of Computing Technology Chinese Academy of Sciences, Sep 2013 - Mar 2021.

    • Technical Consultant. Baidu Inc., Sep 2016 - Feb 2017.

    • Visiting Scholar. Microsoft Research Asia, Apr 2015 - Oct 2015.

      • Working with Xing Xie on Recommender Systems, a senior research manager in Microsoft Research Asia.

    • Assistant Professor. Institute of Computing Technology, CAS, Jul 2011 - Sep 2013.

    • Visiting Student. University of Minnesota Twin Cities (UMN), Sep 2010 - Mar 2011.

      • Working with Prof. George Karypis on Multi-task Learning, a professor at UMN.

    • Visiting Internship Student. Hong Kong University of Science and Technology (HKUST), Jul 2010 - Aug 2010.

      • Working with Prof. Yang Qiang on Transfer Learning, a professor at HKUST.

    Education

    • Ph.D. in Computer Science. Institute of Computing Technology, Chinese Academy of Sciences, July, 2011.

      • Thesis: The Research of Classification Algorithms for Transfer Learning

      • Advisor: Prof. Qing He

    • B.E. in Computer Science. Chongqing University (CQU), 2006.

    Honors and Award

    • Dec. 2020 Excellent researcher in Institute of Computing Technology, CAS

    • Dec. 2019 Excellent researcher in Institute of Computing Technology, CAS

    • Jan. 2017 The Youth Innovation Promotion Association of Chinese Academy of Sciences

    • Jan. 2016 The outstanding researcher in Institute of Computing Technology, CAS

    • Dec. 2012-2015 Excellent researcher in Institute of Computing Technology, CAS (one time per year)

    • Jul. 2015 The Champion of IJCAI 2015 Data Mining Competition (1/753)

    • Oct. 2013 Best Doctoral Dissertation Award, Chinese Association for Artificial Intelligence

    • Dec. 2011 Outstanding Graduate Student, Graduate University of Chinese Academy of Sciences

    • Oct. 2010 Best Paper Candidate & Student Travel Award, ACM CIKM 2010

    • Apr. 2010 Best Paper Candidate, SIAM SDM 2010

    • Jun. 2007-2010 Excellent Student, Chinese Academy of Sciences (one time per year)

    • Jan. 2009 Xia Peisu Scholarship, Institute of Computing Technology

    • Jul. 2006 Excellent Graduate Student, ChongQing City (top 0.5%)

    Research Grants

    • “The Research on Intelligent Recruitment Algorithms Based on Graph Neural Networks”, the National Natural Science Foundation of China, 590,000RMB, 2022.1 - 2025.12.

    • “Study on Open Knowledge Computable Model and Computing Method for Network Big Data”, the National Natural Science Foundation of China, 640,000RMB (2,560,000RMB in total), 2019.1 - 2022.12.

    • “Multi-source Uncertain Data Mining Method and Technique”, the National Key Research and Development Program of China, 600,000RMB (3,420,000RMB in total), 2018.5 - 2021.5.

    • “The Research on Recommendation Algorithms based on Deep Learning”, the National Natural Science Foundation of China, 640,000RMB, 2018.1 - 2021.12.

    • “The Youth Innovation Promotion Association CAS 2017146”, Chinese Academy of Sciences, 800,000RMB, 2017.1 - 2020.12.

    • “The Research on Multi-task Multi-view Learning Algorithms to Heterogeneous Environment”, the National Natural Science Foundation of China, 780,000RMB, 2015.1 - 2018.12.

    • “The Application of Machine Learning Algorithm in Operator Big Data”, ZTE, 200,000RMB, 2017.1 - 2017.12.

    • “2015 Microsoft Research Asia Collaborative Research Program”, MSRA, 100,000RMB, 2016.1 - 2016.12.

    • “The Research on Knowledge Graph Constructing Methods based on Web Data”, Baidu Inc., 100,000RMB, 2016.1 - 2016.12.

    • “The Research of Transfer Learning Algorithms based on Generative Model with Applications”, the National Natural Science Foundation of China, 240,000RMB, 2013.1 - 2015.12.

    • “Research and Development of Public Service Support Technology in Urban Population Life Cycle”, National High-tech R&D Program of China (863 Program), 713,600RMB (4,460,000RMB in total), 2013.1 - 2015.12.

    • “The Develop of Parallel Data Mining System”, Key Lab of Web Data in ICT, 300,000RMB, 2011.8 - 2012.7.