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. O'Donovan, und B. Smyth. IUI '05: Proceedings of the 10th international conference on Intelligent user interfaces, Seite 167--174. New York, NY, USA, ACM Press, (2005)
B. Sarwar, G. Karypis, J. Konstan, und J. Riedl. EC '00: Proceedings of the 2nd ACM conference on Electronic commerce, Seite 158--167. New York, NY, USA, ACM Press, (2000)
J. Schaffer, J. O'Donovan, und T. Höllerer. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, Seite 177--185. New York, NY, USA, ACM, (2018)
G. Schröder, M. Thiele, und W. Lehner. Proceedings of the Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces, Seite 78--85. Chicago, USA, CEUR-WS, (Oktober 2011)