STABLEDR: STABILIZED DOUBLY ROBUST LEARNING FOR RECOMMENDATION ON DATA MISSING NOT AT RANDOM

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.

1. INTRODUCTION

Modern recommender systems (RSs) are rapidly evolving with the adoption of sophisticated deep learning models (Zhang et al., 2019) . However, it is well documented that directly using advanced deep models usually achieves sub-optimal performance due to the existence of various biases in RS (Chen et al., 2020; Wu et al., 2022b) , and the biases would be amplified over time (Mansoury et al., 2020; Wen et al., 2022) . A large number of debiasing methods have emerged and gradually become a trend. For many practical tasks in RS, such as rating prediction (Schnabel et al., 2016; Wang et al., 2020a; 2019) , post-view click-through rate prediction (Guo et al., 2021) , post-click conversion rate prediction (Zhang et al., 2020; Dai et al., 2022) , and uplift modeling (Saito et al., 2019; Sato et al., 2019; 2020) , a critical challenge is to combat the selection bias and confounding bias that leading to significantly difference between the trained sample and the targeted population (Hernán & Robins, 2020) . Various methods were designed to address this problem and among them, doubly robust (DR) methods (Wang et al., 2019; Zhang et al., 2020; Chen et al., 2021; Dai et al., 2022; Ding et al., 2022) play the dominant role due to their better performance and theoretical properties. The success of DR is attributed to its double robustness and joint-learning technique. However, the DR methods still have many limitations. Theoretical analysis shows that inverse probability scoring (IPS) and DR methods may have infinite bias, variance, and generalization error bounds, in the presence of extremely small propensity scores (Schnabel et al., 2016; Wang et al., 2019; Guo et al., 2021; Li et al., 2023b) . In addition, due to the fact that users are more inclined to evaluate the preferred items, the problem of data missing not at random (MNAR) often occurs in RS. This would cause selection bias and results in inaccuracy for methods that more rely on extrapolation, such as error imputation based (EIB) (Marlin et al., 2007; Steck, 2013) and DR methods.

