40 citations · 117 across the 33 of their papers we have counts for
11 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…
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Jun Wang, Haoxuan Li, Chi Zhang +4
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCV…
TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models
Honglei Zhang, Zhiwei Li, Haoxuan Li +3
Federated recommendations (FRs), facilitating multiple local clients to collectively learn a global model without disclosing user private data, have emerged as a prevalent on-devic…
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…
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…
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…