40 citations · 78 across the 9 of their papers we have counts for
4 papers · 2 filters
Multiple Robust Learning for Recommendation
Haoxuan Li, Quanyu Dai, Yuru Li +4
In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and lea…
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
Haoxuan Li, Yan Lyu, Chunyuan Zheng +1
Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doub…
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Yan Lyu, Sunhao Dai, Peng Wu +7
Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item…
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Peng Wu, Haoxuan Li, Yuhao Deng +6
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction a…