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.
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Kim, H.H., Jo, J., Kim, D. (2012). Generation of Tag-Based User Profiles for Clustering Users in a Social Music Site. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28490-8_6
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DOI: https://doi.org/10.1007/978-3-642-28490-8_6
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