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.
|Number of pages||9|
|Journal||Expert Systems with Applications|
|State||Published - May 2009|
|Event||13th Annual ACM International Conference on Multimedia - Singapore, Singapore|
Duration: 6 Nov 2005 → 11 Nov 2005
- Music recommendation; Emotion detection; Affinity discovery
Shan, M-K., Kuo, F-F., Chiang, M-F., & Lee, S-Y. (2009). Emotion-based music recommendation by affinity discovery from film music. Expert Systems with Applications, 36(4), 7666-7674. https://doi.org/10.1016/j.eswa.2008.09.042