A SCALABLE COLLABORATIVE FILTERING ALGORITHM BASED ON LOCALIZED PREFERENCE.pdf

Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July 2008 978-1-4244-2096-4/08/$25.00 ?2008 IEEE 160 A SCALABLE COLLABORATIVE FILTERING ALGORITHM BASED ON LOCALIZED PREFERENCE LIANG ZHANG1, BO XIAO1, JUN GUO1, CHEN ZHU2 1 School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2Economics and Management School, Beijing University of Posts and Telecommunications, Beijing 100876, China E-MAIL: dawnzeye@, xiaobo@, guojun@, ruddymorning@ Abstract: Collaborative filtering has been very successful in both research and applications. The K-Nearest Neighbor (KNN) method is a popular way for its realizations. Its key technique is to find k nearest neighbors for a given user to predict his interests. User-based clustering algorithms of collaborative filtering classify the users into some clusters and select top-N neighbors by using all items to compute similarity in one cluster. Collaborative filtering based on cluster has high scalability but low accuracy of prediction. In this paper we present a new approach to improve the accuracy and the scalability of collaborative filtering. Our approach partition the users, discovered the localized preference in each part and using the localized preference of users to select neighbors for prediction instead of using all items. We present empirical results which show that the method have better satisfactory accuracy and performance. Keywords: Collaborative filtering; Recommender system; Clustering; Localized preference 1. Introduction With the exponential increase of the information of the Internet, Internet trading has become more and more popular. But one main problem that users face is how to find the product they like from millions of products. To aid users in the decision making process, it has become more important to design recommender systems that automatically identify the likely choice