Name
Abstract | ||
|---|---|---|
With the growth of digital music, the development of music recommendation is helpful for users. The existing recommendation approaches are based on the users' preference on music. However, sometimes, recommending music according to the emotion is needed. In this paper, we propose a novel model for emotion-based music recommendation, which is based on the association discovery from film music. We investigated the music feature extraction and modified the affinity graph for association discovery between emotions and music features. Experimental result shows that the proposed approach achieves 85% accuracy in average. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1101149.1101263 | ACM Multimedia 2001 |
Keywords | Field | DocType |
emotion-based music recommendation,existing recommendation approach,film music,association discovery,music feature,digital music,music recommendation,affinity graph,music feature extraction,feature extraction,emotion | Graph,Computer science,Feature extraction,Digital audio,Pop music automation,Multimedia | Conference |
ISBN | Citations | PageRank |
1-59593-044-2 | 26 | 1.47 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang-Fei Kuo | 1 | 171 | 12.53 |
| Meng-Fen Chiang | 2 | 91 | 8.54 |
| Man-Kwan Shan | 3 | 594 | 105.72 |
| Suh-Yin Lee | 4 | 1596 | 319.67 |