A balanced memory-based collaborative filtering similarity measure

Authors:
Jesús Bobadilla;Fernando Ortega;Antonio Hernando;Ángel Arroyo
Affiliations:
Universidad Politécnica of Madrid and FilmAffinity.com research team, Universidad Politécnica of Madrid, crta. of Valencia Km.7, zip 28031, Madrid, Spain;Universidad Politécnica of Madrid and FilmAffinity.com research team, Universidad Politécnica of Madrid, crta. of Valencia Km.7, zip 28031, Madrid, Spain;Universidad Politécnica of Madrid and FilmAffinity.com research team, Universidad Politécnica of Madrid, crta. of Valencia Km.7, zip 28031, Madrid, Spain;Universidad Politécnica of Madrid and FilmAffinity.com research team, Universidad Politécnica of Madrid, crta. of Valencia Km.7, zip 28031, Madrid, Spain
Venue:
International Journal of Intelligent Systems
Year:
2012

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Abstract

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.