Confidence Calibration for Recommender Systems and Its Applications
<|reference_start|>Confidence Calibration for Recommender Systems and Its Applications: 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.<|reference_end|>
@article{kweon2024confidence,
    title={Confidence Calibration for Recommender Systems and Its Applications},
    author={Wonbin Kweon},
    journal={arXiv preprint arXiv:2402.16325},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16325},
    primaryClass={cs.IR}
}