Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational data

Authors:
Katsuhiro Honda;Akira Notsu;Hidetomo Ichihashi
Affiliations:
Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531, Japan.;Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531, Japan.;Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531, Japan
Venue:
International Journal of Knowledge Engineering and Soft Data Paradigms
Year:
2010

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Abstract

Collaborative filtering is a basic technique for tackling information overloads and is composed of task of relating a promising item to an active user. In this paper, a new approach to user-item co-cluster extraction from rectangular relational data is proposed based on the structural balancing concept, and the clustering method is applied to collaborative filtering tasks. In the process, user-item rectangular relational matrix given in an alternative process of 'liking or not' is first transformed into a square adjacency matrix and then co-clusters are sequentially extracted by using a weighted aggregation criterion. In a numerical experiment, the proposed collaborative filtering model is applied to a purchase history data set in order to demonstrate the recommendation ability of the model.