EvoTunes: Crowdsourcing-based music recommendation

Jun Ho Choi; Jong Seok Lee

doi:10.1007/978-3-319-04117-9_32

EvoTunes: Crowdsourcing-based music recommendation

Jun Ho Choi, Jong Seok Lee

School of Integrated Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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	https://doi.org/10.1007/978-3-319-04117-9_32
Publication status	Published - 2014
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

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-04117-9_32

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Choi, J. H., & Lee, J. S. (2014). EvoTunes: Crowdsourcing-based music recommendation. In MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings (PART 2 ed., pp. 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). https://doi.org/10.1007/978-3-319-04117-9_32

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title = "EvoTunes: Crowdsourcing-based music recommendation",

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.",

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Choi, JH & Lee, JS 2014, EvoTunes: Crowdsourcing-based music recommendation. in MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 8326 LNCS, pp. 331-338, 20th Anniversary International Conference on MultiMedia Modeling, MMM 2014, Dublin, Ireland, 14/1/6. https://doi.org/10.1007/978-3-319-04117-9_32

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

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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.

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Choi JH, Lee JS. EvoTunes: Crowdsourcing-based music recommendation. In 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); PART 2). https://doi.org/10.1007/978-3-319-04117-9_32