StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random

Haoxuan Li, Chunyuan Zheng, Peng Wu

Keywords: Recommender System, Bias, Debias, Doubly Robust

TL;DR: This paper proposes a theoretically guaranteed stabilized doubly robust learning approach that overcomes the shortcomings due to the presence of extremely small propensities in debiased recommendations.

Abstract: In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have been widely studied and demonstrate superior performance. However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities. Moreover, the fact that DR relies more on extrapolation will lead to suboptimal performance. To address the above limitations while retaining double robustness, we propose a stabilized doubly robust (StableDR) learning approach with a weaker reliance on extrapolation. Theoretical analysis shows that StableDR has bounded bias, variance, and generalization error bound simultaneously under inaccurate imputed errors and arbitrarily small propensities. In addition, we propose a novel learning approach for StableDR that updates the imputation, propensity, and prediction models cyclically, achieving more stable and accurate predictions. Extensive experiments show that our approaches significantly outperform the existing methods.

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