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Kunhan Wu

2 accepted papers

2024

Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering

ICML 2024spotlight

Recommender system aims to recommend items or information that may interest users based on their behaviors and preferences. However, there may be sampling selection bias in the data collection process, i.e., the collected data is not a representative of the target population. Many debiasing methods…

Cited by 12SourcePDFScholar
2023

Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach

NeurIPS 2023poster

In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning. Previous studies proposed various debiasing methods based on observed user and item features, but ignored the effect of hidden confounding. To addres…

Cited by 36SourcePDFScholar