  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.
