TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
This addresses bias and variance problems in recommender systems for more accurate recommendations, but it is incremental as it builds on existing doubly robust methods.
The paper tackles bias and variance issues in doubly robust methods for debiased recommendations, proposing a principled approach that reduces both simultaneously when error imputation is misspecified, and a semi-parametric collaborative learning method that improves prediction accuracy.
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.