Collaborative Filtering Based on Sequential Extraction of Weighted Use-Item Clusters

Abstract

This paper proposes a new approach to collaborative filtering based on sequential use-item co-cluster extraction, in which personalized information filtering is performed by extracting users and items that are mutually familiar. In order to extract exclusive clusters, a fuzzy clustering method for weighted data is applied to sequential fuzzy cluster extraction. The exclusive constraint is forced only to users so that the clustering process plays a role for connecting promising items to users and achieves appropriate information recommendation.