Social knowledge-driven music hit prediction

verfasst von: Kerstin Bischoff, Claudiu S. Firan, Mihai Georgescu, Wolfgang Nejdl, Raluca Paiu
Abstract: What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches.
Organisationseinheit(en): Forschungszentrum L3S
Typ: Aufsatz in Konferenzband
Seiten: 43-54
Anzahl der Seiten: 12
Publikationsdatum: 2009
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Theoretische Informatik, Allgemeine Computerwissenschaft
Elektronische Version(en): https://doi.org/10.1007/978-3-642-03348-3_8 (Zugang: Geschlossen)

BibTeX

@inproceedings{a05c827c689844a990f0d97102f8d99c,
title = "Social knowledge-driven music hit prediction",
abstract = "What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches.",
keywords = "Classification, Collaborative tagging, Hit songs, Social media",
author = "Kerstin Bischoff and Firan, {Claudiu S.} and Mihai Georgescu and Wolfgang Nejdl and Raluca Paiu",
year = "2009",
doi = "10.1007/978-3-642-03348-3_8",
language = "English",
isbn = "978-3-642-03347-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Berlin Heidelberg",
pages = "43--54",
booktitle = "Advanced Data Mining and Applications",
note = "5th International Conference on Advanced Data Mining and Applications, ADMA 2009 ; Conference date: 17-08-2009 Through 19-08-2009",
}