Peng Wu

According to our database, Peng Wu authored at least 26 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence.

Hang Pan, Shuxian Bi, Wenjie Wang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He

CoRR, 2024

Treatment Effect Estimation for User Interest Exploration on Recommender Systems.

Jiaju Chen, Wenjie Wang, Chongming Gao, Peng Wu, Jianxiong Wei, Qingsong Hua

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

Local Causal Structure Learning in the Presence of Latent Variables.

Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering.

Haoxuan Li, Chunyuan Zheng, Shuyi Wang, Kunhan Wu, Eric Hao Wang, Peng Wu, Zhi Geng, Xu Chen, Xiao-Hua Zhou

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Policy Learning for Balancing Short-Term and Long-Term Rewards.

Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Debiased Collaborative Filtering with Kernel-Based Causal Balancing.

Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference.

Haoxuan Li, Chunyuan Zheng, Sihao Ding, Peng Wu, Zhi Geng, Fuli Feng, Xiangnan He

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Uncovering the Propensity Identification Problem in Debiased Recommendations.

Honglei Zhang, Shuyi Wang, Haoxuan Li, Chunyuan Zheng, Xu Chen, Li Liu, Shanshan Luo, Peng Wu

Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

2023

Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations.

Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu

Proceedings of the ACM Web Conference 2023, 2023

Causal Recommendation: Progresses and Future Directions.

Wenjie Wang, Yang Zhang, Haoxuan Li, Peng Wu, Fuli Feng, Xiangnan He

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach.

Haoxuan Li, Kunhan Wu, Chunyuan Zheng, Yanghao Xiao, Hao Wang, Zhi Geng, Fuli Feng, Xiangnan He, Peng Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach.

Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu, Xiangnan He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation.

Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Trustworthy Policy Learning under the Counterfactual No-Harm Criterion.

Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu, Peng Wu

Proceedings of the International Conference on Machine Learning, 2023

Propensity Matters: Measuring and Enhancing Balancing for Recommendation.

Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Peng Cui

Proceedings of the International Conference on Machine Learning, 2023

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random.

Haoxuan Li, Chunyuan Zheng, Peng Wu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations.

Haoxuan Li, Yan Lyu, Chunyuan Zheng, Peng Wu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Multiple Robust Learning for Recommendation.

Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Xiao-Hua Zhou, Peng Wu

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random.

Haoxuan Li, Chunyuan Zheng, Xiao-Hua Zhou, Peng Wu

CoRR, 2022

Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random.

Peng Wu, Haoxuan Li, Yan Lyu, Xiao-Hua Zhou

CoRR, 2022

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems.

Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, Yuhao Deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou

CoRR, 2022

Causal Analysis Framework for Recommendation.

Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou

CoRR, 2022

Accepted Tutorials at The Web Conference 2022.

Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lye, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He

Proceedings of the Companion of The Web Conference 2022, Virtual Event / Lyon, France, April 25, 2022

Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis.

Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, Yongdong Zhang

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction.

Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Rui Zhang, Jie Sun

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges.

Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022