An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms

Jon Herlocker, Joseph A Konstan, John Riedl

Research output: Contribution to journalArticlepeer-review

555 Scopus citations


Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content indexing or content analysis, collaborative filtering systems rely entirely on interest ratings from members of a participating community. Since predictions are based on human ratings, collaborative filtering systems have the potential to provide filtering based on complex attributes, such as quality, taste, or aesthetics. Many implementations of collaborative filtering apply some variation of the neighborhood-based prediction algorithm. Many variations of similarity metrics, weighting approaches, combination measures, and rating normalization have appeared in each implementation. For these parameters and others, there is no consensus as to which choice of technique is most appropriate for what situations, nor how significant an effect on accuracy each parameter has. Consequently, every person implementing a collaborative filtering system must make hard design choices with little guidance. This article provides a set of recommendations to guide design of neighborhood-based prediction systems, based on the results of an empirical study. We apply an analysis framework that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component. The three components identified are similarity computation, neighbor selection, and rating combination.

Original languageEnglish (US)
Pages (from-to)287-310
Number of pages24
JournalInformation Retrieval
Issue number4
StatePublished - Oct 2002


  • Collaborative filtering
  • Empirical studies
  • Information filtering
  • Preference prediction


Dive into the research topics of 'An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms'. Together they form a unique fingerprint.

Cite this