00000cab a22000003a 4500 UP-99796217608500935 Buklod 20231007233935.0 m |o d | ta 080901s xx d | ||r ||||| || (iLib)UPD-00048495483 DENGII eng Herlocker, Jonathan L. Evaluating collaborative filtering recommender systems. Vol. 22, no. 1 pp. 5 - 53 Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated. Collaborative filtering. Evaluation. Metrics. Recommender systems. Konstan, Joseph A. Terveen, Loren G. Riedl, John T. ACM transactions on information systems. 22, 1 (2004). FO UPD DENG-II Article