Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Generation of Tag-Based User Profiles for Clustering Users in a Social Music Site
verfasst von : Hyon Hee Kim, Jinnam Jo, Donggeon Kim
Erschienen in: Intelligent Information and Database Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.