Emotion-based music recommendation by affinity discovery from film music

Man-Kwan Shan, Fang-Fei Kuo, Meng-Fen Chiang, Suh-Yin Lee

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user's preference on music. However, sometimes, it might better meet users' requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average. (C) 2008 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)7666-7674
Number of pages9
JournalExpert Systems with Applications
Issue number4
StatePublished - May 2009
Event13th Annual ACM International Conference on Multimedia - Singapore, Singapore
Duration: 6 Nov 200511 Nov 2005


  • Music recommendation; Emotion detection; Affinity discovery

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