Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
The Collaborative Filtering Recommendation Based on Similar-Priority and Fuzzy Clustering
PEITS '08 Proceedings of the 2008 Workshop on Power Electronics and Intelligent Transportation System
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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Recommender systems can help people to find interesting things and they are widely used in Electronic Commerce. Collaborative filtering technique has been proved to be one of the most successful techniques in recommender systems. The main problems of collaborative filtering are about prediction accuracy, response time, data sparsity and scalability. To solve some of these problems, this paper presented an item-based collaborative filtering recommendation algorithm using self-organizing map. Firstly, it employs clustering function of self-organizing map to form nearest neighbors of the target item. Then, it produces prediction of the target user to the target item using item-based collaborative filtering. The item-based collaborative filtering recommendation algorithm using self-organizing map can efficiently improve the scalability and promise to make recommendations more accurately than conventional collaborative filtering.