• Data Mining
  • Recommender Systems
  • Data Mining and User Modeling in Social Multimeida Platforms
  • 2024.03 One paper has been accepted by SIGIR, focusing on multimodality invariant learning for multimedia-based recommendation.
  • 2024.03 One paper has been accepted by TOIS, focusing on user-side counterfactual fairness for collaborative filtering based recommendation.
  • 2024.03 One paper has been accepted by TOIS, with the topic of interpretable knowledge tracing for intelligent education.
  • 2024.02 One paper has been accepted by AI Open, focusing on label-aware debiased learning for natural language inference.
  • 2023.12 One paper has been accepted by TKDE, with the topic of hyperbolic graph learning for social recommendation.
  • 2023.11 One paper has been accepted by TBD, with the topic of attribute-enhanced recommendation.
  • 2023.09 Two papers have been accepted by NeurIPS, with topics of disentanglement learning for cognitive diagnosis and fairness-aware user modeling under limited sensitve attributes.
  • 2023.09 One paper has been accepted by TKDE, with the topic of neighborhood-enhanced contrastive learning for recommendation.
  • 2023.07 One paper has been accepted by MM, with the topic of generative cold-start recommendation.
  • 2023.05 One paper has been accepted by TNNLS, with the topic of label enhanced self- supervised learning for text classification.
  • 2023.05 One paper is accepted by KDD 2023, with the topic of robust exercise recommendation.
  • 2023.04 Three papers have been accepted by SIGIR, with two long papers focusing on graph based recommendation, and a resource paper on recommendation studio.
  • 2023.01 One paper has been accepted by WWW, with the topic of data level debias for recommendation!

[WWW 2023] Lei Chen, Le Wu*, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang. Improving Recommendation Fairness via Data Augmentation.  [PDF] [Code]
In this paper, we studied the recommendation fairness issue from data augmentation perspective. Given the original training data, we proposed a FDA framework to generate fake user behavior data, in order to improve recommendation fairness. FDA can be applied to any embedding based recommendation backbones, and does not rely on any specifc fairness metrics. Extensive experiments on two real-world datasets clearly showed FDA is efective to balance recommendation accuracy and fairness under different recommendation backbones.


[NeurIPS 2023] Xiangzhi Chen, Le Wu*, Fei Liu, Lei Chen, Kun Zhang, Richang Hong, Meng Wang. Disentangling Cognitive Diagnosis with Limited Exercise Labels.  [PDF] [Code]
In this paper, we present a novel approach, called DCD, to tackle the interpretability problem of cognitive diagnosis with limited exercise labels. Inspired by semi-supervised DRL, we learn disentangled representations and align them with real limited labels by group-based disentanglement and limited-labeled alignment. Extensive experiments on widely used benchmarks demonstrate the superiority of our proposed method in few-labeled scenarios.

Conference Papers

  • Haoyue Bai, Le Wu*, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong and Meng Wang. Multimodality Invariant Learning for Multimedia-based New Item Recommendation. The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), accepted, 2024.

  • Xiangzhi Chen, Le Wu*, Fei Liu, Lei Chen, Kun Zhang, Richang Hong, Meng Wang. Disentangling Cognitive Diagnosis with Limited Exercise Labels. The 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 18028-18045, December 10-16, New Orleans, LA, USA.  [PDF] [Code]

  • Haoyue Bai, Min Hou*, Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Meng Wang. GoRec: A Generative Cold-Start Recommendation Framework. The 31th ACM International Conference on Multimedia (ACM MM 2023), 1004-1012, October 29-November 3, New York, NY, USA.  [PDF] [Code]

  • Fei Liu, Xuegang Hu*, Shuochen Liu, Chenyang Bu, Le Wu*. Meta Multi-Agent Exercise Recommendation: A Game Application Perspective. The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023), 1441-1452, August 6-10, Long Beach, CA, USA.  [PDF]

  • Shuai Jie, Le Wu*, Kun Zhang, Peijie Sun, Richang Hong, Meng Wang. Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), 1188–1197, July 23-27, Taipei, Taiwan.  [PDF]

  • Yonghui Yang, Zhengwei Wu, Le Wu*, Kun Zhang, Richang Hong, Zhiqiang Zhang, Jun Zhou, Meng Wang. Generative-Contrastive Graph Learning for Recommendation. The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), 1117–1126, July 23-27, Taipei, Taiwan.  [PDF] [Code]

  • Lei Chen, Le Wu*, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, Meng Wang. Improving Recommendation Fairness via Data Augmentation. The Web Conference (WWW 2023), 1012-1020, April 30 - May 4, Austin, USA.  [PDF] [Code]

  • Chen Zhao, Le Wu*,Pengyang Shao, Kun Zhang, Richang Hong, Meng Wang. Fair Representation Learning for Recommendation: A Mutual Information Perspective. The 37th AAAI Conference on Artificial Intelligence (AAAI 2023), 4911-4919, February 7-14, Washington DC, USA.  [PDF] [Code]

  • Minghao Zhao, Le Wu*, Yile Liang, Lei Chen, Jian Zhang, Qilin Deng, Kai Wang, Xudong Shen, Tangjie Lv, Runze Wu. Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), 50-59, July 11-15, Madrid, Spain.  [PDF]

  • Jie Shuai, Kun Zhang, Le Wu*, Peijie Sun, Richang Hong, Meng Wang, Yong Li. A Review-aware Graph Contrastive Learning Framework for Recommendation. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), 1283–1293, July 11-15, Madrid, Spain.  [PDF] [Code]

  • Yonghui Yang, Le Wu*, Richang Hong, Kun Zhang, Meng Wang. Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021) , 71-80, July 11-15, online.  [PDF] [Code]

  • Lei Chen, Le Wu*, Kun Zhang, Richang Hong, Meng Wang. Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), 585-594, July 11-15, online.  [PDF] [Code]

  • Shuai Wang, Kun Zhang*, Le Wu, Haiping Ma, Richang Hong, Meng Wang. Privileged Graph Distillation for Cold Start Recommendation. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), 1187-1196, July 11-15, online.  [PDF]

  • Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, Meng Wang. Learning Fair Representations for Recommendation: A Graph based Perspective. The Web Conference (WWW 2021), 2198–2208, April 19-23, China.  [PDF] [Code]

  • Kun Zhang, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, Shulan Ruan. Making the Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching. The 34th AAAI Conference on Artificial Intelligence (AAAI 2021), 14411-14419, February 2-9, Virtual Conference.  [PDF]

  • Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, Meng Wang. Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach. The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), 679-688, July 25-30, Xi’an, China.  [PDF] [Code]

  • Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang. Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation. The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), 1211-1220, Xi’an, China.  [PDF]

  • Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, Meng Wang. Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation. The Web Conference (WWW 2020), 837-847, April 20-24, Taipei, China.  [PDF] [Code]

  • Lei Chen, Le Wu*, Richang Hong, Kun Zhang, Meng Wang. Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. The 34th AAAI Conference on Artificial Intelligence (AAAI 2020), 27-34, February 7-12, New York, USA.  [PDF] [Code]

  • Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang. A Neural Influence Diffusion Model for Social Recommendation. The 42th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), 235-244, July 21-25, Pairs, France. [PDF] [Code]

  • Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang. Personalized Multimedia Item and Key Frame Recommendation. The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 1431-1437, August 10-16, Macao, China.  [PDF]

  • Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W Zheng, Qi Liu. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach. The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 4681-4688, August 10-16, Macao, China.  [PDF]

  • Peijie Sun, Le Wu*, Meng Wang. Attentive Recurrent Social Recommendation. The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), 185-194, July 8-12, Ann Anbor, MI, USA.  [PDF]

  • Xunpeng Huang, Le Wu, Enhong Chen, Hengshu Zhu, Qi Liu, Yijun Wang. Incremental Matrix Factorization: A Linear Feature Transformation Perspective. The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), 1901-1908, Melbourne, Australia, August 19-25, 2017.  [PDF]

  • Le Wu, Yong Ge, Qi Liu, Enhong Chen, Bai Long, Zhenya Huang. Modeling Users Preferences and Social Links in Social Networking Services: a Joint-Evolving Perspective, The 30th AAAI Conference on Artificial Intelligence (AAAI 2016), 279-286, Phoenix, Arizona USA, February 12-17, 2016.  [PDF]

  • Le Wu, Qi Liu, Enhong Chen, Xing Xie, Chang Tang. Product Adoption Prediction: A Multi-factor View, The 15th SIAM International Conference on Data Mining (获最佳论文提名,Best of SDM 2015) , 154-162, Vancouver, BC, Canada, April 30-May 2, 2015.  [PDF]

  • Le Wu, Yin Zhu, Nicholas Jing Yuan, Enhong Chen, Xing Xie, Yong Rui. Predicting Smartphone Adoption Social Networks. The 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), 472-485, Ho Chi Minh City, Vietnam, May 19-22, 2015.  [PDF]

  • Le Wu, Enhong Chen, Qi Liu, Linli Xu, Tengfei Bao, Lei Zhang. Leveraging Tagging for Neighborhood-aware Probabilistic Matrix Factorization, The 21st ACM Conference on Information and Knowledge Management (CIKM 2012), 1854-1858, Maui, HI, USA, October 29-November 02, 2012.  [PDF]

Journal Papers

  • Pengyang Shao, Le Wu*, Kun Zhang, Defu Lian, Richang Hong, Yong Li, Meng Wang*. Average User-side Counterfactual Fairness for Collaborative Filtering. ACM Transactions on Information Systems (ACM TOIS), accepted, 2024.  [PDF]

  • Fei Liu, Chengyang Bu, Haotian Zhang, Le Wu, Kui Yu, Xuegang Hu. FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning. ACM Transactions on Information Systems (ACM TOIS), early access, 2024.  [PDF] [Code]

  • Kun Zhang, Dacao Zhang, Le Wu, Richang Hong, Ye Zhao, Meng Wang. Label-aware debiased causal reasoning for Natural Language Inference. AI Open, 5:70-78, March 2024.  [PDF] [Code]

  • Yonghui Yang, Le Wu*, Kun Zhang, Richang Hong, Hailin Zhou, Zhiqiang Zhang, Jun Zhou, Meng Wang. Hyperbolic Graph Learning for Social Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted, 2023.  [PDF] [Code]

  • Peijie Sun, Le Wu*, Kun Zhang, Xiangzhi Chen, Meng Wang. Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), accepted, 2023.  [PDF]

  • Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang. A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 35(5): 4425-4445, May 2023.  [PDF]

  • Pengyang Shao, Le Wu*, Lei Chen, Kun Zhang, Meng Wang. FairCF: Fairness-aware Collaborative Filtering. SCIENCE CHINA Information Sciences (SCIS), 65(12): 222102, December 2022.  [PDF] [Code]

  • Peijie Sun, Le Wu*, Kun Zhang, Yu Su, Meng Wang. An Unsupervised Aspect-aware Recommendation Model with Explanation Text Generation. ACM Transactions on Information Systems (ACM TOIS), 40(3):1-29, July 2022.  [PDF] [Code]

  • Le Wu, Junwei Li, Peijie Sun, Richang Hong, Yong Ge, Meng Wang. DiffNet++: A Neural Influence and Interest Diffusion Model for Social Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 34(10):4753-4766, October, 2022.  [PDF] [Code]

  • Junwei Li, Le Wu*, Richang Hong, Kun Zhang, Yong Ge, Yan Li. A Joint Neural Model for User Behavior Prediction on Social Networking Platforms. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 11(6):1-25, December 2020.  [PDF]

  • Richang Hong, Yuan He, Le Wu*, Yong Ge, Xindong Wu. Deep Attributed Network Embedding by Preserving Structure and Attribute Information. IEEE Transactions on Systems Man and Cybernetics: Systems (IEEE TSMC: Systems), 51(3):1434-1445, March 2021.  [PDF]

  • Le Wu, Peijie Sun, Richang Hong, Yong Ge, Meng Wang. Collaborative Neural Social Recommendation. IEEE Transactions on Systems Man and Cybernetics: Systems (IEEE TSMC: Systems), 51(1):464-476, January 2021.  [PDF]

  • Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang. A Hierarchical Attention Model for Social Contextual Image Recommendation. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 32(10):1854-1867, October 2020.  [PDF] [Code]

  • Lei Chen, Le Wu, Zhenzhen Hu, Meng Wang. Quality-aware Unpaired Image-to-image Translation. IEEE Transactions on Multimedia (IEEE TMM), 21(10):2664-2674, October 2019.  [PDF]

  • Le Wu, Qi Liu, Richang Hong, Enhong Chen, Yong Ge, Xing Xie, Meng Wang. Product Adoption Rate Prediction in a Competitive Market. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 30(2):325-338, February 2018.  [PDF]

  • Le Wu, Yong Ge, Qi Liu, Enhong Chen, Richang Hong, Junping Du, Meng Wang. Modeling the Evolution of Users' Preferences and Social Links in Social Networking Services. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 29(6): 1240-1253, June 2017.  [PDF]

  • Le Wu, Qi Liu, Enhong Chen, Nicholas Jing Yuan, Guangming Guo, Xing Xie. Relevance meets Coverage: A Unified Framework to Generate Diversified Recommendation. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 7(3): 39:1-39:30, February 2016.  [PDF]

  • 孙光福,吴乐,刘淇,朱琛,陈恩红. 基于时序行为的协同过滤推荐算法. 软件学报,vol. 24(11), pages: 2721-2733, 2013.  [PDF]
  • Member of Special Interest Group on Information Retrieval, Chinese Information Processing Society of China
  • Member of Special Interest Group on Artificial Intelligence and Pattern Recognition, China Computer Federation
  • Member of IEEE, ACM
  • Conference Reviewers:
  • Senior PC: AAAI 2021, IJCAI 2021
  • PC: AAAI, IJCAI, WWW, NeurIPS, KDD, CIKM, MM, ICDM, et al.
    • Journal Reviewers:
  • TKDE, TNNLS, TOIS, TSMC: Systems, TMC, TIST et al.
  • SCIENTIA SINICA Informationis, Frontiers of Computer Sience, 计算机学报, 软件学报
    • 2021.01 Youth Talent Promotion Project, China Association for Science and Technology
    • 2020.05 Youth Talent Promotion Project, Anhui Association for Science and Technology
    • 2017.07 Outstanding Dissertation Award, Chinese Association for Artificial Intelligence
    • 2017.05 Star-Track Visiting Program, Microsoft Research
    • 2015.03 Best of SDM paper Award, SIAM SDM 2015
    • 2013.08 Outstanding Student Paper Award, The 17th China Conference on Machine Learning