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Bias is a common problem inherent in recommender systems, which is entangledwith users' preferences and poses a great challenge to unbiased learning. Fordebiasing tasks, the doubly robust (DR) method and its variants show superiorperformance due to the double robustness property, that is, DR is unbiased wheneither imputed errors or learned propensities are accurate. However, ourtheoretical analysis reveals that DR usually has a large variance. Meanwhile,DR would suffer unexpectedly large bias and poor generalization caused byinaccurate imputed errors and learned propensities, which usually occur inpractice. In this paper, we propose a principled approach that can effectivelyreduce bias and variance simultaneously for existing DR approaches when theerror imputation model is misspecified. In addition, we further propose a novelsemi-parametric collaborative learning approach that decomposes imputed errorsinto parametric and nonparametric parts and updates them collaboratively,resulting in more accurate predictions. Both theoretical analysis andexperiments demonstrate the superiority of the proposed methods compared withexisting debiasing methods.

TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations