40 citations · 78 across the 9 of their papers we have counts for
9 papers
A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimators
Jiayi Guo, Haoxuan Li, Ye Tian +1
While significant progress has been made in heterogeneous treatment effect (HTE) estimation, the evaluation of HTE estimators remains underdeveloped. In this article, we propose a…
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
Haoxuan Li, Yanghao Xiao, Chunyuan Zheng +1
Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale o…
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
Quanyu Dai, Haoxuan Li, Peng Wu +5
Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the m…
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…
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
Haoxuan Li, Chunyuan Zheng, Peng Wu
In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning o…
CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU
Zangwei Zheng, Pengtai Xu, Xuan Zou +12
The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerati…
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…