Method of Collaborative Filtering Based on Uncertain User Interests Cluster

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. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read. In the paper, we introduce the uncertain interests of users because computer logs take down the data that have uncertain features. Consequently, user interest data recorded in computer logs is uncertain data. First of all the definition of uncertain interests are put forward. Then, some method using a clustering algorithm can solve the uncertain feature. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, because calculation input is uncertainty, the results of clustering algorithm are latent non-determinacy. The trustworthy degree of uncertain interests is defined that it can measure the rationality of clustering algorithm results. Thirdly, improvement of collaborative filtering is presented as an advanced CF algorithm based on trustworthy degree of uncertain interests. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.