An Effective Algorithm for Dimensional Reduction in Collaborative Filtering
Fengrong Gao1, Chunxiao Xing1, and Yong Zhao2
1Research Institute of Information Technology, Tsinghua University, Beijing 100084
gaofengrong@mail.tsinghua.edu.cn
xingcx@mail.tsinghua.edu.cn
2Department of Computer Science and Technology, Tsinghua University, Beijing 100084
Zhaoyong04@mails.tsinghua.edu.cn
Abstract. It is necessary to provide personalized information service for users through the enormous volume of information on the web. Collaborative filtering is the most successful recommender system technology to date and is used in many domains. Unfortunately collaborative filtering is limited by the high dimensionality and sparsity of user-item rating matrix. In this paper, we propose a new method for applying semantic classification to collaborative filtering. Experimental results show the high efficiency and performance of our approach, compared with tradition collaborative filtering algorithm and collaborative filtering using K-means clustering algorithm.
Keywords: Collaborative filtering, dimensionality reduction, semantic classification LNCS 4822, p. 75 ff.
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