Improvement of naive Bayes collaborative filtering using interval estimation

Recommender systems emerged to help users choose among the large amount of options that ecommerce sites offer. Collaborative filtering is one of the most successful recommender techniques. Here we propose an approach to collaborative filtering based on the simple Bayesian classifier. We propose a method of increasing the efficiency of naive Bayes by applying a new semi naive Bayes approach based on interval estimation. To evaluate our algorithm we use a database of Microsoft anonymous Web data from the UCl repository. Our empirical results show that our proposed Interval based naive Bayes approach outperforms typical naive Bayes.