H. Chen, K. Furuse, N. Ohbo, and S. Nishihara (Japan)
Collaborative Filter, Information Searches, Knowledge Discovery, Digital Library, Cluster ing.
Collaborative filtering has been very successful in appli cations such as E-commerce and digital library. In this paper we propose a new approach for improving collab orative filtering by W-clustering (Voters and Voted-items clustering). That is, items are clustered and users are also clustered with respect to item clusters. Collaborative filter ing is then performed on cluster-cluster relationship which overcomes several problems which traditional methods are suffered from. More specifically, comparing to tradition methods, cluster-cluster collaborative filtering avoids the problem of sparsity and the so called cold start problem, and enhances the accuracy of finding neighbor users hence results in a more effective collaborative filtering. Experi ments on the EachMovie data set show favorable results.
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