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.
N. Felicioni, M. Dacrema, und P. Cremonesi. Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Seite 10-15. ACM, (Juni 2021)
Y. Wang, L. Wang, Y. Li, D. He, und T. Liu. Proceedings of the 26th Annual Conference on Learning Theory, Volume 30 von Proceedings of Machine Learning Research, Seite 25--54. Princeton, NJ, USA, PMLR, (Juni 2013)
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)
M. Dacrema, P. Cremonesi, und D. Jannach. (2019)cite arxiv:1907.06902Comment: Source code available at: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation.
A. Bellogin, P. Castells, und I. Cantador. Proceedings of the fifth ACM conference on Recommender systems - RecSys 2011, Seite 333 -- 336. ACM Press, (2011)
M. Ge, C. Delgado-Battenfeld, und D. Jannach. Proceedings of the fourth ACM conference on Recommender systems - RecSys \textquotesingle10, Seite 257-260. ACM Press, (2010)
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)