A personalized recommendation system combining case-based reasoning and user-based collaborative filtering

Personalized recommendation systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is one of the most important technologies in recommender systems. Such technology recommends items to a customer according to the preference data of similar customers. The main problems of collaborative filtering are about prediction accuracy and data sparsity. To solve these problems, this paper presents a personalized recommendation algorithm combining case-based reasoning and user-based collaborative filtering. Firstly, it employs case-based reasoning technology to fill the vacant ratings of the user-item matrix. Then, it produces prediction of the target user to the target item using user-based collaborative filtering. The personalized recommendation system combining case-based reasoning and user-based collaborative filtering can alleviate the sparsity issue and can produce more accuracy recommendation than the traditional recommender systems.