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A User-Item Predictive Model for Collaborative Filtering Recommendation

  • Heung-Nam Kim
  • Ae-Ttie Ji
  • Cheol Yeon
  • Geun-Sik Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

Collaborative Filtering recommender systems, one of the most representative systems for personalized recommendations in E-commerce, enable users to find the useful information easily. But traditional CF suffers from some weaknesses: scalability and real-time performance. To address these issues, we present a novel model-based CF approach to provide efficient recommendations. In addition, we propose a new method of building a model with dynamic updates, when users present explicit feedback. The experimental evaluation on MovieLens datasets shows that our method offers reasonable prediction quality as good as the best of user-based Pearson correlation coefficient algorithm.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Heung-Nam Kim
    • 1
  • Ae-Ttie Ji
    • 1
  • Cheol Yeon
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent E-Commerce Systems Lab., Dept. of Information Engineering, Inha University 
  2. 2.School of Information Engineering, Inha University, IncheonKorea

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