Abstract
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.
Abstract (translated)
在推荐系统中，大量评分因为选择偏差而缺失，这种现象被称为随机缺失。实际反概率评价(IPS)被用来计算每个观测评分的估计误差，尽管在多个情况下有效，但我们认为由于估计概率的不确定性校准不足，IPS估计的性能受到限制。在本文中，我们提议使用多个代表性不确定性校准技术来校准推荐系统中的估计概率校准。对偏差和泛化限制的理论分析表明，校准后的IPS估计器比未校准的更加优越。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进，因此带来了更好的推荐结果。
URL
https://arxiv.org/abs/2303.12973
