  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.
