期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2021
卷号：99
期号：17
语种：English
出版社：Journal of Theoretical and Applied
摘要：Recently, the Recommender system is the most important research area with the advent of e-commerce and e-business on the web. Emotion-based music recovery will have extraordinary potential in catering nowadays, digital music archives quickly extending in the developing smartphones and ubiquitous environments. Many types of research are conducted to improve the music recommendation to users based on their emotions. Human emotions have much difficulty due to the subjective perception of emotions and accuracy challenges. In this paper, we need to solve the problem of recommending songs to the user based on his selection if it was bad, sad, or angry mood by using our system we will recommend to the user songs from pleasant mood to try changing him to the good mood and track if user listen to this song or scaped it. Our new algorithm, "Hybrid emotion-based music recommendation system," will recommend music to the next level, generating playlist which suits and matches your mood of listening to music. The user can try three choices to get the emotion by using face recognition, choosing three colors, and using the arousal map to select the emotion will appear to users then recommended songs according to his status we merge the output of the system to detect the right mood. Our new system has good novelty and diversity of songs recommended to users and changes the user's mood to the pleasure. At our experimental results We are using precision, recall and f-measure accuracy equations to calculate the effective of our system. To gain high results we apply different experiments detect users� emotions like using face only, colors, arousal map then let users select to types of emotion like face and colors or colors and arousal and finally apply hybrid emotions system. Every time we measure the accuracy of the results. Based on the experiments results using our new hybrid emotions model is best accuracy in surprised, anger, natural and relaxed. While user�s emotion sadness using face. arousal map has high accuracy with happy emotions.
