Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Papers
Collaborative Filtering Based on Sequential Extraction of User-Item Clusters
Katsuhiro HondaAkira NotsuHidetomo Ichihashi
Author information
JOURNALS FREE ACCESS

2009 Volume 22 Issue 10 Pages 364-370

Details
Abstract

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

Information related to the author
© 2009 The Institute of Systems, Control and Information Engineers
Previous article
feedback
Top