• Information Systems Review
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• v.8 no.2
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• pp.139-154
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• 2006

Recommender systems help users find their interesting items more easily or provide users with meaningful items based on their preferences. Collaborative filtering(CF) recommender systems, the most successful recommender system, use opinions of users to recommend for an active user who needs recommendation. That is, ratings which users have voted on items to indicate preference on them are the source for making recommendation. Although CF systems are designed only to use users' preferences as the source of recommendation, use of some available information is believed to increase both the performance and the accuracy of CF systems. In this paper, we propose a CF recommender system which utilizes both degree of match and demographic information(e.g., occupation, gender, age) to increase the performance and the accuracy. Since more and more information is accumulated in CF systems, it is important to reduce the data volume while maintaining the same or the higher level of accuracy. We used both degree of match and demographic information as criteria for reducing the data volume, thereby naturally enhancing the performance. It is shown that using degree of match improves the prediction accuracy too in CF systems and also that using some demographic information also results in better accuracy.