Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate. However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce bias and variance simultaneously for existing DR approaches when the error imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing methods.

翻译：偏差是推荐系统中常见的固有问题，它会与用户偏好交织，给无偏学习带来巨大挑战。针对去偏任务，双稳健（DR）方法及其变体因具备双稳健性（即当插补误差或学习到的倾向性任一准确时，DR估计无偏）而展现出优越性能。然而，我们的理论分析揭示，DR通常具有较大方差。同时，实际中由不准确的插补误差和学习到的倾向性（这种情况常发生）导致的偏差激增与泛化能力下降，会使DR面临严峻挑战。本文提出一种原则性方法，能在误差插补模型设定错误的情况下，有效同时降低现有DR方法的偏差与方差。此外，我们进一步提出了一种新颖的半参数协作学习方法，将插补误差分解为参数部分与非参数部分，并通过协作更新提升预测精度。理论分析与实验均表明，与现有去偏方法相比，所提方法具有明显优势。