Debiased collaborative filtering aims to learn an unbiased prediction model
by removing different biases in observational datasets. To solve this problem,
one of the simple and effective methods is based on the propensity score, which
adjusts the observational sample distribution to the target one by reweighting
observed instances. Ideally, propensity scores should be learned with causal
balancing constraints. However, existing methods usually ignore such
constraints or implement them with unreasonable approximations, which may
affect the accuracy of the learned propensity scores. To bridge this gap, in
this paper, we first analyze the gaps between the causal balancing requirements
and existing methods such as learning the propensity with cross-entropy loss or
manually selecting functions to balance. Inspired by these gaps, we propose to
approximate the balancing functions in reproducing kernel Hilbert space and
demonstrate that, based on the universal property and representer theorem of
kernel functions, the causal balancing constraints can be better satisfied.
Meanwhile, we propose an algorithm that adaptively balances the kernel function
and theoretically analyze the generalization error bound of our methods. We
conduct extensive experiments to demonstrate the effectiveness of our methods,
and to promote this research direction, we have released our project at
this https URL

通过在可观测数据集中消除不同的偏差，去偏协同过滤旨在学习一个无偏的预测模型。本文通过重新加权观测样本分布以适应目标样本的倾向得分来解决此问题。为了更好地满足因果平衡约束条件，作者提出了一种近似平衡函数的方法，并在再生核希尔伯特空间中证明了其更好满足因果平衡约束条件的能力。同时，作者提出了一种自适应平衡核函数的算法，并理论上分析了方法的泛化误差界。通过广泛的实验证明了该方法的有效性。