An Item Based Collaborative Filtering Recommendation Algorithm Using Rough Set Prediction

Abstract

Recommender systems represent personalized services that aim at predicting users’ interest on information items available in the application domain. Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users’ ratings is the major reason causing the poor quality. To solve this problem, this paper proposed an item based collaborative filtering recommendation algorithm using the rough set theory prediction. This method employs rough set theory to fill the vacant ratings of the user-item matrix where necessary. Then it utilizes the item based collaborative filtering to produce the recommendation. The experiments were made on a common data set using different filtering algorithms. The results show that the proposed recommender algorithm combining rough set theory and item based collaborative filtering can improve the accuracy of the collaborative filtering recommendation system.