Collaborative Filtering Using Interval Estimation Naïve Bayes

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 naive 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 naive Bayes approach based on intervals. In this work we present this new approach.