Journal of applied mathematics & informatics

한국전산응용수학회 (The Korean Society for Computational and Applied Mathematics)
권호 표지

CLASSIFICATION FUNCTIONS FOR EVALUATING THE PREDICTION PERFORMANCE IN COLLABORATIVE FILTERING RECOMMENDER SYSTEM

  • Lee, Seok-Jun (Department of Management Information System, Sangji University) ;
  • Lee, Hee-Choon (Department of Computer Data Information, Sangji University) ;
  • Chung, Young-Jun (Department of Computer Science, Kangwon National University)
  • 발행 : 2010.01.30
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초록

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

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참고문헌

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