40 citations · 88 across the 20 of their papers we have counts for
7 papers · 1 filter
A Self-Triggered Agentic Push Recommendation System
Zhao-Yu Zhang, Qingying Chen, Chunyuan Zheng +9
Push notification is a critical recommendation scenario on large-scale platforms, allowing the system to proactively reach users outside the application to improve long-term re-eng…
Debiased Recommendation with Noisy Feedback
Haoxuan Li, Chunyuan Zheng, Wenjie Wang +3
Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased l…
Debiased Collaborative Filtering with Kernel-Based Causal Balancing
Haoxuan Li, Chunyuan Zheng, Yanghao Xiao +4
Debiased collaborative filtering aims to learn an unbiased prediction model by removing different biases in observational datasets. To solve this problem, one of the simple and eff…
Pareto Invariant Representation Learning for Multimedia Recommendation
Shanshan Huang, Haoxuan Li, Qingsong Li +2
Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations intro…
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Haoxuan Li, Taojun Hu, Zetong Xiong +4
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety. Recently, many studies…
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…
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…