GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Semantic web recommender systems
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
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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.