EvoTunes: Crowdsourcing-based music recommendation

Jun Ho Choi, Jong Seok Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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 languageEnglish
Title of host publicationMultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Proceedings
Number of pages8
EditionPART 2
Publication statusPublished - 2014
Event20th Anniversary International Conference on MultiMedia Modeling, MMM 2014 - Dublin, Ireland
Duration: 2014 Jan 62014 Jan 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8326 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other20th Anniversary International Conference on MultiMedia Modeling, MMM 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'EvoTunes: Crowdsourcing-based music recommendation'. Together they form a unique fingerprint.

Cite this