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Improvement on Similarity Calculation in Collaborative Filtering Recommendation using Demographic Information  

이용준 (한국전기연구원 전기시험연구소)
이세훈 (인하공업전문대학 컴퓨터정보공학부)
왕창종 (인하대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.9, no.5, 2003 , pp. 521-529 More about this Journal
Abstract
In this paper we present an improved method by using demographic information for overcoming the similarity miss-calculation from the sparsity problem in collaborative filtering recommendation systems. The similarity between a pair of users is only determined by the ratings given to co-rated items, so items that have not been rated by both users are ignored. To solve this problem, we add virtual neighbor's rating using demographic information of neighbors for improving prediction accuracy. It is one kind of extentions of traditional collaborative filtering methods using the peason correlation coefficient. We used the Grouplens movie rating data in experiment and we have compared the proposed method with the collaborative filtering methods by the mean absolute error and receive operating characteristic values. The results show that the proposed method is more efficient than the collaborative filtering methods using the pearson correlation coefficient about 9% in MAE and 13% in sensitivity of ROC.
Keywords
Recommendation System; Demographic Information; Collaborative Filtering;
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