Details
| Original language | English |
|---|---|
| Title of host publication | Advanced Data Mining and Applications |
| Subtitle of host publication | 5th International Conference, ADMA 2009, Proceedings |
| Pages | 43-54 |
| Number of pages | 12 |
| ISBN (electronic) | 978-3-642-03348-3 |
| Publication status | Published - 2009 |
| Event | 5th International Conference on Advanced Data Mining and Applications, ADMA 2009 - Beijing, China Duration: 17 Aug 2009 → 19 Aug 2009 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5678 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
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
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Advanced Data Mining and Applications: 5th International Conference, ADMA 2009, Proceedings. 2009. p. 43-54 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5678 LNAI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Social knowledge-driven music hit prediction
AU - Bischoff, Kerstin
AU - Firan, Claudiu S.
AU - Georgescu, Mihai
AU - Nejdl, Wolfgang
AU - Paiu, Raluca
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Classification
KW - Collaborative tagging
KW - Hit songs
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=70350339343&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03348-3_8
DO - 10.1007/978-3-642-03348-3_8
M3 - Conference contribution
AN - SCOPUS:70350339343
SN - 978-3-642-03347-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 43
EP - 54
BT - Advanced Data Mining and Applications
T2 - 5th International Conference on Advanced Data Mining and Applications, ADMA 2009
Y2 - 17 August 2009 through 19 August 2009
ER -