Constructing full matrix through Naïve Bayesian for collaborative filtering

Authors:
Kyung-Yong Jung;Hee-Joung Hwang;Un-Gu Kang
Affiliations:
School of Computer Information Engineering, Sangji University, Korea;Department of Information Technology Engineering, Gachon University of Medicine and Science, Korea;Department of Information Technology Engineering, Gachon University of Medicine and Science, Korea
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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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.