Item-Based Collaborative Filtering Recommendation using Self-Organizing Map

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.