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.

本论文针对推荐系统中存在的缺失数据，提出了一种基于反事实倾向评分的缺失值加权估计方法。通过使用多种代表性不确定性校准技术来对倾向得分估计进行不确定性校准，理论分析和实验结果表明，不确定性校准的 IPS 估计器比未校准的表现更好，从而提高了推荐结果。