Abstract
Collaborative filtering is a technique for achieving personalized recommendations in a network community, and a promising collaborative filtering approach is based on user or item clustering. In the previous study, we proposed a sequential co-clustering model for extracting user-item groups, where users and items are mutually connected, and showed that the user-item connections support high recommendation ability. However, the computational cost of extracting user-item co-clusters is high because eigenvalue problems of a (user + item) square matrix must be solved. In the present study, we consider the reduction of computational cost by taking into account the characteristics of the input data matrix, the off-diagonal block elements of which are all zero.
