Recommender systems emerged to help users choose among the large amount of options that e-commerce sites offer. Collaborative filtering is one of the most successful recommender techniques. In this paper we propose an approach to collaborative filtering based on the simple Bayesian classifier. We propose a method of increasing the efficiency of naïve bayes by applying a new semi naïve Bayes approach based on interval estimation. To evaluate our algorithm we use a database of Microsoft Anonymous Web Data from the UCI repository. Our empirical results show that our proposed interval based naïve Bayes approach outperforms typical naïve bayes¹.