Evaluating collaborative filtering recommender systems

Jonathan L. Herlocker, Joseph A Konstan, Loren G Terveen, John T. Riedl

Research output: Contribution to journalReview articlepeer-review

3816 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)5-53
Number of pages49
JournalACM Transactions on Information Systems
Volume22
Issue number1
DOIs
StatePublished - Jan 2004

Keywords

  • Collaborative filtering
  • Evaluation
  • Metrics
  • Recommender systems

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