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.
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Robles, V., Larrañaga, P., Peña, J.M., Marbán, O., Crespo, J., Pérez, M.S. (2003). Collaborative Filtering Using Interval Estimation Naïve Bayes. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds) Advances in Web Intelligence. AWIC 2003. Lecture Notes in Computer Science, vol 2663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44831-4_6
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DOI: https://doi.org/10.1007/3-540-44831-4_6
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