Abstract
Due to the large number of users of the mobile teaching terminal and the many types of music teaching resources, the recommendation accuracy is low. To this end, this paper proposes a personalized recommendation method for online music teaching resources based on mobile terminals. This paper identifies the characteristics of online music teaching resources, connects the resources through knowledge points, and optimizes the streaming media storage format using mobile terminals. The time continuous signal is converted into discrete time signal, and the user interest model is constructed by collaborative filtering, and the favorite resources of neighbor users are recommended to the current user. The experimental results show that the accuracy of this method is 75.694%, 66.669% and 66.350%, respectively, which shows that the performance of this method is better than the other two methods.
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References
Bing, C.: Distance teaching system of public music course based on SOA service framework. Mod. Sci. Instruments 6, 27–30 (2020)
Wang, Z., Jianhua, L.: Research on the rapid recommendation model of online teaching resources in colleges and universities. Inf. Stud. Theory Appl. 44(5), 180–186 (2021)
Chen, X.: A methodology study of enhanced college education reform powered by online education resourcing. Guide Sci. Educ. 27(1), 8–9 (2020)
Geping, L., Xing, W.: Reshaping online education by virtual reality: learning resources, teaching organization and system platform. China Educ. Technol. 11, 87–96 (2020)
Zhang, J., Hao, W., Ban, W., Rong, J.: An optimal design of vocal music teaching platform based on virtual reality system. Comput. Simul. 38(06), 160–164 (2021)
Zhang, J.-X., Huang, S.-L., Liu, L.-J., et al.: Research on equipment identification based on machine vision in mobile terminals. Fire Control Command Control, 45(2), 155–159 (2020)
Nie, L., Juan, F., Chengqi, Y., et al.: Measuring enterprise's offline resumption with mobile device positioning data. Data Anal. Knowl. Discov. 4(7), 38–49 (2020)
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lin, H., Lin, Y., Huang, H. (2023). Personalized Recommendation Method of Online Music Teaching Resources Based on Mobile Terminal. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-28867-8_26
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DOI: https://doi.org/10.1007/978-3-031-28867-8_26
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