Generation of tag-based user profiles for clustering users in a social music site

Authors:
Hyon Hee Kim;Jinnam Jo;Donggeon Kim
Affiliations:
Department of Statistics and Information Science, Dongduk Women's University, Seoul, South Korea;Department of Statistics and Information Science, Dongduk Women's University, Seoul, South Korea;Department of Statistics and Information Science, Dongduk Women's University, Seoul, South Korea
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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Abstract

Collaborative tagging has become increasingly popular as a powerful tool for a user to present his opinion about web resources. In this paper, we propose a method to generate tag-based profiles for clustering users in a social music site. To evaluate our approach, a data set of 1000 users was collected from last.fm, and our approach was compared with conventional track-based profiles. The K-Means clustering algorithm is executed on both user profiles for clustering users with similar musical taste. The test of statistical hypotheses of inter-cluster distances is used to check clustering validity. Our experiment clearly shows that tag-based profiles are more efficient than track-based profiles in clustering users with similar musical tastes.