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arXiv · 2402.16325

Confidence Calibration for Recommender Systems and Its Applications

Wonbin Kweon
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Abstract

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.

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Wonbin Kweon. 2024-02-26. Confidence Calibration for Recommender Systems and Its Applications. https://arxiv.org/abs/2402.16325

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