A collaborative filtering incorporating hybrid-clustering technology

Personalized recommendation systems can help people find interesting things and are widely used in developing the Internet. Many recommendation systems employ collaborative filtering technology, which has proven one of the most successful techniques in recommender systems in recent years. Collaborative filtering is a method of making predictions about the interests of a user by collecting preferences or taste information from many users. However, this technique suffers poor quality when the number of missing preferences or taste information in the user database increases. To resolve the problems in collaborative filtering, traditional approach presents collaborative filtering using cluster-based data smoothing. However, this approach didn't use data smoothing in the entire the user database, and most of the clustering method is user clustering, in addition, they use clustering including missing value. Therefore, this paper presents a novel approach that incorporates hybrid-clustering technology after introducing a smooth-based method in the entire database. First, we introduce rating smoothing for the entire database based on collaborative-filtering algorithms that uses similarities among users are called item-based collaborative filtering to resolve the sparsity issue. The proposed approach utilizes clustering collaborative filtering that combines both user clustering and item clustering to produce the recommendations. Finally, we evaluate the experiment results in point of the precision.