Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational data
Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi · International Journal of Knowledge Engineering and Soft Data Paradigms · 2010
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.