The Collaborative Filtering Recommendation Mechanism Based on Bayesian Theory

In this paper, we propose a Collaborative filtering recommendation method based on Bayesian theory. It firstly divides the items that has been rated into two group, then uses Bayesian theory to study the users´ preference. And analyze the degrees of the users´ preference for the items´ inherent characteristics. Then judge which group the item that has not been rated belongs to. At last It computes the similarities of ratings in the cluster which it belong to. Because it searches less, it can improve the response time. The problem of scalability and Real-time was resolved. Finally, we experimentally evaluate our result and compare them to the traditional item-based algorithms. Our experiments showed that this algorithm could effectively improve the real-time performance of recommendation systems.