Accurate prediction in session-based recommendation has achieved progress, but skewed recommendation list caused by popularity bias is rarely investigated. Existing models on mitigating the popularity bias try to reduce the over-concentration on popular items but ignore the users' different preferences towards tail items. To this end, we incorporate calibration to mitigate the popularity bias in session-based recommendation. We propose a calibration module that can predict the distribution of the recommendation list and calibrate the recommendation list to the ongoing session. Meanwhile, a separate training and prediction strategy is applied to deal with the imbalance problem. Experiments on benchmark datasets show that our model can both achieve the competitive accuracy of recommendation and provide more tail items.

翻译：对届会建议所作的准确预测已取得进展,但很少调查受欢迎偏差造成的偏差建议清单。现有的减少受欢迎偏差模式试图减少受欢迎物品的过度集中,但忽视用户对尾品的不同偏好。为此,我们将校准纳入会议建议,以减少受欢迎偏差。我们提议了一个校准模块,可以预测建议清单的分布情况,并将建议清单校准到正在进行的届会。与此同时,还采用单独的培训和预测战略来处理不平衡问题。基准数据集实验显示,我们的模型既能达到建议的竞争准确性,又能提供更多尾品。