Abstract
In recent days, there have been many attempts to automatically recommend music clips that are expected to be liked by a listener. In this paper, we present a novel music recommendation system that automatically gathers listeners' direct responses about the satisfaction of playing specific two songs one after the other and evolves accordingly for enhanced music recommendation. Our music streaming web service, called "EvoTunes," is described in detail. Experimental results using the service demonstrate that the success rate of recommendation increases over time through the proposed evolution process.
| Original language | English |
|---|---|
| Title of host publication | MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings |
| Pages | 331-338 |
| Number of pages | 8 |
| Edition | PART 2 |
| DOIs | |
| Publication status | Published - 2014 Feb 7 |
| Event | 20th Anniversary International Conference on MultiMedia Modeling, MMM 2014 - Dublin, Ireland Duration: 2014 Jan 6 → 2014 Jan 10 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 8326 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 20th Anniversary International Conference on MultiMedia Modeling, MMM 2014 |
|---|---|
| Country | Ireland |
| City | Dublin |
| Period | 14/1/6 → 14/1/10 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
EvoTunes : Crowdsourcing-based music recommendation. / Choi, Jun Ho; Lee, Jong Seok.
MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings. PART 2. ed. 2014. p. 331-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8326 LNCS, No. PART 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - EvoTunes
T2 - Crowdsourcing-based music recommendation
AU - Choi, Jun Ho
AU - Lee, Jong Seok
PY - 2014/2/7
Y1 - 2014/2/7
N2 - In recent days, there have been many attempts to automatically recommend music clips that are expected to be liked by a listener. In this paper, we present a novel music recommendation system that automatically gathers listeners' direct responses about the satisfaction of playing specific two songs one after the other and evolves accordingly for enhanced music recommendation. Our music streaming web service, called "EvoTunes," is described in detail. Experimental results using the service demonstrate that the success rate of recommendation increases over time through the proposed evolution process.
AB - In recent days, there have been many attempts to automatically recommend music clips that are expected to be liked by a listener. In this paper, we present a novel music recommendation system that automatically gathers listeners' direct responses about the satisfaction of playing specific two songs one after the other and evolves accordingly for enhanced music recommendation. Our music streaming web service, called "EvoTunes," is described in detail. Experimental results using the service demonstrate that the success rate of recommendation increases over time through the proposed evolution process.
UR - http://www.scopus.com/inward/record.url?scp=84893467127&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893467127&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04117-9_32
DO - 10.1007/978-3-319-04117-9_32
M3 - Conference contribution
AN - SCOPUS:84893467127
SN - 9783319041162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 331
EP - 338
BT - MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings
ER -