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 \url{https://github.com/DongHande/AutoDebias}.
翻译:推荐者系统依靠用户行为数据,如评级和点击等,以建立个性化模型。然而,所收集的数据是观察性的,而不是实验性的,在数据中造成各种偏差,对所学模型有重大影响。大多数现有的建议偏差工作,如反向偏差评分和估算方法,侧重于一到两个具体的偏差,缺乏能够说明数据中混合或甚至未知偏差的普遍能力。但是,为了缩小这一研究差距,我们首先从\ textit{风险差异}的角度分析偏差的来源,这代表了预期的经验风险和真实风险之间的差别。值得注意的是,我们得出了一个总体学习框架,通过说明一般框架的某些参数来很好地概括现有偏差战略。通过深度分析,我们通过深度分析,将当前数据排序和深度分析,将当前数据排序推向另一个方向。通过深度分析,我们通过深度分析,将当前数据调整到当前水平,通过深度分析,将数据排序为最优化,将数据排序为最优化。