ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Joining Case-Based Reasoning and Item-Based Collaborative Filtering in Recommender Systems
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01
Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents
Engineering Applications of Artificial Intelligence
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
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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.