Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction.
J. Schaffer, J. O'Donovan, and T. Höllerer. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, page 177--185. New York, NY, USA, ACM, (2018)
Y. Wang, L. Wang, Y. Li, D. He, and T. Liu. Proceedings of the 26th Annual Conference on Learning Theory, volume 30 of Proceedings of Machine Learning Research, page 25--54. Princeton, NJ, USA, PMLR, (June 2013)