Item-Based Collaborative Filtering Recommendation using Self-Organizing Map

摘要：

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.

关键词： Collaborative Filtering Recommender System Self-organizing Map

作者: SongJie Gong HongWu Ye XiaoMing Zhu

作者单位: Zhejiang Business Technology Institute, Ningbo 315012, P. R. China Zhejiang Textile & Fashion College, Ningbo 315211, P. R. China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 4029-4031

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）