Debiased Recommendation via Wasserstein Causal Balancing
Wang, Hao Chen, Zhichao Zhang, Honglei Li, Zhengnan Pan, Licheng Li, Haoxuan Gong, Mingming
Wang, Hao
Chen, Zhichao
Zhang, Honglei
Li, Zhengnan
Pan, Licheng
Li, Haoxuan
Gong, Mingming
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
License
Language
English
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Abstract
Recommendation systems are pivotal in improving user experience on various digital platforms. However, observational training data in recommendation systems introduce selection bias, which leads to a distributional discrepancy between training data and real-world scenarios, resulting in suboptimal performance. Current causal debiasing methods such as inverse propensity score and doubly robust rely on accurately estimated propensity scores, typically optimized through negative log-likelihood (NLL) minimization. However, recent studies have highlighted the limitations of this approach, as perfect NLL minimization may not adequately correct for selection bias. To address this issue, we propose Wasserstein Balancing Metric (WBM), a novel metric that measures and enhances the balancing capacity of propensity scores in causal debiasing methods by minimizing the Wasserstein discrepancy between reweighted populations. On the basis, we introduce IPS-WBM and DR-WBM, incorporating WBM as a regularizer in standard inverse propensity score and doubly robust estimators, which enhances causal balancing capacity without introducing additional bias. Extensive experiments on three real-world recommendation datasets demonstrate that our methods improve the causal balancing capability of learned propensities and enhance debiasing performance.
Citation
WangHao et al., “Debiased Recommendation via Wasserstein Causal Balancing,” ACM Trans Inf Syst, Oct. 2024, doi: 10.1145/3725731
Source
ACM Transactions on Information Systems
Conference
Keywords
Recommender system, Causal inference, Selection bias, Entire space, Multi-task learning, Post-click conversion rate estimation
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Source
Publisher
Association for Computing Machinery