Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

Abstract

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.