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MusicTagger: Exploiting User Generated Game Data for Music Recommendation

Identifieur interne : 000656 ( Main/Exploration ); précédent : 000655; suivant : 000657

MusicTagger: Exploiting User Generated Game Data for Music Recommendation

Auteurs : Hannes Olivier [Allemagne] ; Marc Waselewsky [Allemagne] ; Niels Pinkwart [Allemagne]

Source :

RBID : ISTEX:4761BB58816C4D74845AFDACD1B459AB1BD1B43C

Abstract

Abstract: The system “MusicTagger” is a game in which two players hear 30 seconds of a song, describe it independently and get points if they succeed in making the same descriptions. Additionally, it is a music recommendation system which compares songs with the help of the descriptions given in the game. MusicTagger is based on the principle of “human computation”, meaning that problems (in this case, music recommendation) are solved by computers via eliciting human knowledge and making intelligent use of the aggregated information. This paper presents the design and implementation of the “MusicTagger” system together with results of an empirical lab study which demonstrates the potential of the recommendation engine.

Url:
DOI: 10.1007/978-3-642-21619-0_80


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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