Author:Peng Wu

Publications
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 and Xiao-Hua Zhou EasyChair Preprint 13096
Propensity Matters: Measuring and Enhancing Balancing for Recommendation Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu and Peng Cui EasyChair Preprint 13095
Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu and Peng Cui EasyChair Preprint 13094
Trustworthy Policy Learning Under the Counterfactual No-Harm Criterion Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu and Peng Wu EasyChair Preprint 13093
Fairly Recommending with Social Attributes: a Flexible and Controllable Optimization Approach Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu and Xiangnan He EasyChair Preprint 13092
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 and Peng Wu EasyChair Preprint 13091
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations Haoxuan Li, Yan Lyu, Chunyuan Zheng and Peng Wu EasyChair Preprint 13090
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random Haoxuan Li, Chunyuan Zheng and Peng Wu EasyChair Preprint 13089
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations Haoxuan Li, Yanghao Xiao, Chunyuan Zheng and Peng Wu EasyChair Preprint 13088

Keyphrases

bias⁴, causal inference², causal learning, Counterfactual², Debias⁴, Debiased recommender system, Doubly Robust², fairness, Individual treatment effects, multi-task learning, Optimal treatment regime, policy learning, Propensity, randomized controlled trials, Recommendation, Recommender System⁶, social, Trustworthy AI, Unobserved Confounding.