A method for personalised music recommendation based on emotional multi-label
by Yuan Luo; Qiuji Chen
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 15, No. 2, 2023

Abstract: In this paper, a personalised music recommendation method based on emotion multi-label was proposed. First is the analysis of music emotion and music emotional label, then, the principal component analysis method is used to reduce the dimension to process the music features and complete the preprocessing. Secondly, construct the music emotion multi-label, and combine the cosine method to calculate the emotional multi-label similarity. Finally, the interest degree of emotional multi-label is calculated to obtain the user's interest degree of music resources, and the personalised recommendation method is optimised to realise the personalised recommendation of music. Experimental results show that the average coverage rate of personalised music recommendation of the proposed method is as high as 99.5%, the accuracy is 98.3%, and the recommendation time of 500 music items is only 18.9 s. Therefore, the recommendation effect of the proposed method is good, the accuracy of personalised music recommendation is improved, and the recommendation time is shortened.

Online publication date: Thu, 06-Apr-2023

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