Abstract
The Semantic Web offers huge amounts of structured and linked data about various different kinds of resources. We propose to use this data for music recommender systems following a storytelling approach. Beyond similarity of audio content and user preference profiles, recommender systems based on Semantic Web data offer opportunities to detect similarities between artists based on their biographies, musical activities, etc. In this paper we present an approach determining similar artists based on freely available metadata from the Semantic Web. An evaluation experiment has shown that our approach leads to more high quality novel artist recommendations than well-known systems such as Last.fm and Echo Nest. However the overall recommendation accuracy leaves room for further improvement.
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Baumann, S., Schirru, R., Streit, B. (2011). Towards a Storytelling Approach for Novel Artist Recommendations. In: Detyniecki, M., Knees, P., Nürnberger, A., Schedl, M., Stober, S. (eds) Adaptive Multimedia Retrieval. Context, Exploration, and Fusion. AMR 2010. Lecture Notes in Computer Science, vol 6817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27169-4_1
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DOI: https://doi.org/10.1007/978-3-642-27169-4_1
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