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 na ¨ ıve 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 na¨ ıve Bayes approach based on intervals. In this work we present this new approach. ,