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Constructing Full Matrix Through Naïve Bayesian for Collaborative Filtering

  • Kyung-Yong Jung
  • Hee-Joung Hwang
  • Un-Gu Kang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Collaborative filtering systems based on a matrix are effective in recommending items to users. However, these systems suffer from the fact that they decrease the accuracy of recommendations, recognized specifically as the sparsity and the first rater problems. This paper proposes the constructing full matrix through Naïve Bayesian, to solve the problems of collaborative filtering. The proposed approach uses Naïve Bayesian, in order to convert the sparse ratings matrix into a full ratings matrix; subsequently using collaborative filtering, to provide recommendations. The proposed method is evaluated in the EachMovie dataset and the approach is demonstrated to perform better than both collaborative filtering and content-based filtering.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyung-Yong Jung
    • 1
  • Hee-Joung Hwang
    • 2
  • Un-Gu Kang
    • 2
  1. 1.School of Computer Information Engineering, Sangji UniversityKorea
  2. 2.Department of Information Technology Engineering, Gachon University of Medicine and ScienceKorea

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