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Sequential user-item weighted-cluster extraction for Collaborative filtering

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3 Author(s)
Katsuhiro Honda ; Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, 599-8531, Japan ; Akira Notsu ; Hidetomo Ichihashi

This paper proposes a new approach to collaborative filtering, in which sequential user-item cluster extraction is performed in order to relate the items to be recommended to each user. In the process, a user-item rectangular relational matrix whose elements are defined by an alternative process of “liking or not“ is first transformed into a square adjacency matrix and then co-clusters are sequentially extracted using a weighted aggregation criterion. Numerical examples including an application to a purchase history data set demonstrate the characteristics of the proposed approach.

Published in:

World Automation Congress (WAC), 2010

Date of Conference:

19-23 Sept. 2010