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Constructing Full Matrix Through Naïve Bayesian for Collaborative Filtering

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Computational Intelligence (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4114))

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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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© 2006 Springer-Verlag Berlin Heidelberg

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Jung, KY., Hwang, HJ., Kang, UG. (2006). Constructing Full Matrix Through Naïve Bayesian for Collaborative Filtering. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_150

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  • DOI: https://doi.org/10.1007/978-3-540-37275-2_150

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37274-5

  • Online ISBN: 978-3-540-37275-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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