Integrating Feature Information for Improving Accuracy of Collaborative Filtering

Collaborative filtering (CF) has been widely used and successfully applied to recommend items in practical applications. However, the collaborative filtering has two inherent problems: data sparseness and the cold- start problems. In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome those difficulties and improve the accuracy of recommendation. We apply a two-pass method, first filling in unknown preference values, then generating the top-N recommendations.

Authors and Affiliations

1. Department of Computer Engineering, Dongguk University, Seoul, Korea

   Hyungil Kim & Juntae Kim

2. Department of Computer Science, Oregon State University, Corvallis, Oregon, USA

   Jonathan L. Herlocker

Editors and Affiliations

1. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

   Chengqi Zhang

2. Department of Computer Science, Auckland University of Technology, 1020, Auckland, New Zealand

   Hans W. Guesgen

3. Artificial Intelligence Technology Centre, Auckland University of Technology, Auckland, New Zealand

   Wai-Kiang Yeap

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© 2004 Springer-Verlag Berlin Heidelberg

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