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,很大一部分评分都会丢失,这被称为缺失非随机性。反事实的倒数倾向得分(IPS)被用来权衡所有观察到的评分的估计误差。虽然在多种场景中具有有效性,但我们认为IPS估计的性能受到倾向度估计中不确定性校准的限制。在本文中,我们提出了不确定性校准,以应用于推荐系统中估计倾向度的多种代表性不确定性校准技术。对偏差和泛化界限的理论分析表明,与未校准的估计器相比,经校准的IPS估计器具有优越性。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,从而带来了更好的推荐结果。