Fuzzy Co-Clustering and Application to Collaborative Filtering

Cooccurrence information analysis became more popular in many web-based system analyses such as document analysis or purchase history analysis. Rather than the conventional multivariate observations, each object is characterized by its cooccurrence degrees with various items, and the goal is often to extract co-cluster structures among objects and items, such that mutually familiar object-item pairs form a co-cluster. A typical application of co-cluster structure analysis can be seen in collaborative filtering (CF). CF is a basic technique for achieving personalized recommendation in various web services by considering the similarity of preferences among users. This paper introduces a fuzzy co-clustering model, which is motivated from a statistical co-clustering model, and demonstrates its applicability to CF tasks following a brief review of the CF framework.