Collaborative filtering using interval estimation naïve Bayes

Authors:
V. Robles;P. Larrañaga;J. M. Peña;O. Marbán;J. Crespo;M. S. Pérez
Affiliations:
Department of Computer Architecture and Technology, Technical University of Madrid, Madrid, Spain;Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastián, Spain;Department of Computer Architecture and Technology, Technical University of Madrid, Madrid, Spain;Department of Computer Science, University Carlos III of Madrid, Madrid, Spain;Department of Computer Science, University Carlos III of Madrid, Madrid, Spain;Department of Computer Architecture and Technology, Technical University of Madrid, Madrid, Spain
Venue:
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Year:
2003

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Abstract

Personalized recommender systems can be classified into three main categories: content-based, mostly used to make suggestions depending on the text of the web documents, collaborative filtering, that use ratings from many users to suggest a document or an action to a given user and hybrid solutions. In the collaborative filtering task we can find algorithms such as the naïve Bayes classifier or some of its variants. However, the results of these classifiers can be improved, as we demonstrate through experimental results, with our new semi naïve Bayes approach based on intervals. In this work we present this new approach.