Collaborative Filtering by Sequential Extraction of User-Item Clusters Based on Structural Balancing Approach

This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized recommendation is performed by finding user-item neighborhoods (co-clusters) from a rectangular relational data matrix, in which users and items have mutually positive relations. In the proposed approach, user-item clusters are extracted one by one in a sequential manner via a structural balancing technique, used in conjunction with the sequential fuzzy cluster extraction method.