A SCALABLE COLLABORATIVE FILTERING ALGORITHM BASED ON LOCALIZED PREFERENCE

摘要：

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.

关键词： Collaborative filtering Recommender system Clustering Localized preference

作者: LIANG ZHANG BO XIAO JUN GUO CHEN ZHU

作者单位: School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing 10087 Economics and Management School, Beijing University of Posts and Telecommunications, Beijing 100876,

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 160-167

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）