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Article type: Research Article
Authors: Ma, Nanaa; * | Wang, Lilia | Long, Yutingb; *
Affiliations: [a] School of Art, Jiaozuo University, Jiaozuo, Henan, China | [b] Huaiyin Institute of Technology, Huai’an, China
Correspondence: [*] Corresponding author. Nana Ma, School of Art, Jiaozuo University, Jiaozuo 454000, Henan, China. E-mail: manana168@outlook.com and Yuting Long, Huaiyin Institute of Technology, Email: Huai’an 223003, China. long_yuting@outlook.com.
Abstract: Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals.
Keywords: Music teaching management, fuzzy logic, recommender system, clustering and similarity
DOI: 10.3233/JIFS-232422
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
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