Recommender systems rely on user behavior data like ratings and clicks to
build personalization model. However, the collected data is observational
rather than experimental, causing various biases in the data which
significantly affect the learned model. Most existing work for recommendation
debiasing, such as the inverse propensity scoring and imputation approaches,
focuses on one or two specific biases, lacking the universal capacity that can
account for mixed or even unknown biases in the data. Towards this research
gap, we first analyze the origin of biases from the perspective of \textit{risk
discrepancy} that represents the difference between the expectation empirical
risk and the true risk. Remarkably, we derive a general learning framework that
well summarizes most existing debiasing strategies by specifying some
parameters of the general framework. This provides a valuable opportunity to
develop a universal solution for debiasing, e.g., by learning the debiasing
parameters from data. However, the training data lacks important signal of how
the data is biased and what the unbiased data looks like. To move this idea
forward, we propose \textit{AotoDebias} that leverages another (small) set of
uniform data to optimize the debiasing parameters by solving the bi-level
optimization problem with meta-learning. Through theoretical analyses, we
derive the generalization bound for AutoDebias and prove its ability to acquire
the appropriate debiasing strategy. Extensive experiments on two real datasets
and a simulated dataset demonstrated effectiveness of AutoDebias. The code is
available at https://github.com/DongHande/AutoDebias.

基于 risk-discrepancy 的架构能很好的概括现有去偏见策略，并提出一个 meta-learning 算法 AutoDebias 用于训练，通过对两个真实数据集和一个模拟数据集的验证，AutoDebias 证明了其在去偏见方面的有效性。