In recommendation systems, a large portion of the ratings are missing due to the selection biases, which is known as Missing Not At Random. The counterfactual inverse propensity scoring (IPS) was used to weight the imputation error of every observed rating. Although effective in multiple scenarios, we argue that the performance of IPS estimation is limited due to the uncertainty miscalibration of propensity estimation. In this paper, we propose the uncertainty calibration for the propensity estimation in recommendation systems with multiple representative uncertainty calibration techniques. Theoretical analysis on the bias and generalization bound shows the superiority of the calibrated IPS estimator over the uncalibrated one. Experimental results on the coat and yahoo datasets shows that the uncertainty calibration is improved and hence brings the better recommendation results.

翻译：在推荐系统中，由于选择偏差的存在，大量的评分数据缺失，这被称为非随机缺失。反事实逆倾向评分（IPS）被用于对每个观测评分的插补误差进行加权。尽管IPS在多种场景下有效，但我们认为其估计性能受到倾向估计不确定性校准不足的限制。本文提出了针对推荐系统中倾向估计的不确定性校准方法，并采用了多种具有代表性的不确定性校准技术。关于偏差和泛化界的理论分析表明，校准后的IPS估计器优于未校准的估计器。在Coat和Yahoo数据集上的实验结果表明，不确定性校准得到了改善，从而带来了更优的推荐结果。