Emotion-based music recommendation by association discovery from film music

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. Categories and Subject Descriptors H.5.5 [Information Interfaces and Presentation]: Sound and Music Computing – methodologies and techniques; J.4 [Computer Applications]: Social and Behavioral Sciences – psychology. General Terms Algorithms, Human Factors. Keywords music recommendation, emotion, affinity graph, association discovery

• Type: Conference
• Year: 2005
• Where: MM
• Authors: Fang-Fei Kuo, Meng-Fen Chiang, Man-Kwan Shan, Suh-Yin Lee
• Keywords: Association Discovery, MM 2005, Music Features, Music Recommendation