Debiased Recommendation via Wasserstein Causal Balancing

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 ..