Emotion-based music recommendation by affinity discovery from film music Export

@article{Shan2009music-recom, abstract = {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.}, address = {Tarrytown, NY, USA}, author = {Shan, Man K. and Kuo, Fang F. and Chiang, Meng F. and Lee, Suh Y.}, citeulike-article-id = {3440292}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1508324.1508576}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.eswa.2008.09.042}, citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S0957417408006581}, day = {23}, doi = {10.1016/j.eswa.2008.09.042}, issn = {0957-4174}, journal = {Expert Syst. Appl.}, keywords = {emotion, music, recommendation}, month = {September}, number = {4}, pages = {7666--7674}, posted-at = {2009-11-09 14:51:37}, priority = {2}, publisher = {Pergamon Press, Inc.}, title = {Emotion-based music recommendation by affinity discovery from film music}, url = {http://dx.doi.org/10.1016/j.eswa.2008.09.042}, volume = {36}, year = {2009} }

TY - JOUR ID - Shan2009music-recom L3 - citeulike-article-id:3440292 N2 - 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. IS - 4 JF - Expert Syst. Appl. SN - 0957-4174 AD - Tarrytown, NY, USA EP - 7674 TI - Emotion-based music recommendation by affinity discovery from film music PB - Pergamon Press, Inc. VL - 36 SP - 7666 KW - emotion KW - music KW - recommendation AU - Shan, Man AU - Kuo, Fang AU - Chiang, Meng AU - Lee, Suh PY - 2009/09/23/ UR - http://dx.doi.org/10.1016/j.eswa.2008.09.042 ER -