An effective algorithm for dimensional reduction in collaborative filtering

Authors:
Fengrong Gao;Chunxiao Xing;Yong Zhao
Affiliations:
Research Institute of Information Technology, Tsinghua University, Beijing;Research Institute of Information Technology, Tsinghua University, Beijing;Department of Computer Science and Technology, Tsinghua University, Beijing
Venue:
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Year:
2007

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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.