Despite the importance of having a measure of confidence in recommendation results, it has been surprisingly overlooked in the literature compared to the accuracy of the recommendation. In this dissertation, I propose a model calibration framework for recommender systems for estimating accurate confidence in recommendation results based on the learned ranking scores. Moreover, I subsequently introduce two real-world applications of confidence on recommendations: (1) Training a small student model by treating the confidence of a big teacher model as additional learning guidance, (2) Adjusting the number of presented items based on the expected user utility estimated with calibrated probability.

翻译：尽管在推荐结果中具有置信度度量至关重要，但与推荐准确性相比，这一领域在文献中却出人意料地被忽视。在本论文中，我提出了一种面向推荐系统的模型校准框架，旨在基于学习到的排序分数，准确估计推荐结果的置信度。此外，我随后介绍了置信度在推荐中的两个实际应用：(1) 通过将大型教师模型的置信度作为额外学习指导来训练小型学生模型；(2) 基于校准概率估计的预期用户效用，调整呈现项目的数量。