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Location-Adapted Music Recommendation Using Tags

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User Modeling, Adaption and Personalization (UMAP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6787))

Abstract

Context-aware music recommender systems are capable to suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context aware recommendation task — selecting music content that fits a place of interest (POI). To address this problem we have used emotional tags attached by a users’ population to both music and POIs. Moreover, we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. In order to test our hypothesis, i.e., that the users will reckon that a music track suits a POI when this track is selected by our approach, we have designed a live user experiment where subjects are repeatedly presented with POIs and a selection of music tracks, some of them matching the presented POI and some not. The results of the experiment show that there is a strong overlap between the users’ selections and the best matching music that is recommended by the system for a POI.

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Kaminskas, M., Ricci, F. (2011). Location-Adapted Music Recommendation Using Tags. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-22362-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22361-7

  • Online ISBN: 978-3-642-22362-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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