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Title

Collaborative Filtering by User-Item Clustering Based on Structural Balancing Approach

Author

Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi

Vol. 8  No. 12  pp. 190-195

Abstract

Collaborative filtering is a technique for reducing information overload and is achieved by predicting the applicability of items to users. In neighborhood-based algorithms, the applicability is predicted by the weighted averages of ratings of neighbors. This paper considers a new approach to user-item clustering in collaborative filtering. The new clustering method plays a role for selecting the user-item neighbors based on a structural balance theory used in social science, in which users and items are partitioned into two clusters by balancing a general signed graph composed of alternative evaluations on items by users.

Keywords

Collaborative filtering, Clustering, Signed graph, Perceptual balance

URL

http://paper.ijcsns.org/07_book/200812/20081227.pdf