MusicTagger: Exploiting User Generated Game Data for Music Recommendation
Identifieur interne : 000656 ( Main/Exploration ); précédent : 000655; suivant : 000657MusicTagger: Exploiting User Generated Game Data for Music Recommendation
Auteurs : Hannes Olivier [Allemagne] ; Marc Waselewsky [Allemagne] ; Niels Pinkwart [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
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:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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