Evaluating collaborative filtering recommender systems

  title={Evaluating collaborative filtering recommender systems},
  author={Jonathan L. Herlocker and Joseph A. Konstan and Loren G. Terveen and John Riedl},
  journal={ACM Trans. Inf. Syst.},
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… CONTINUE READING
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