A personalized recommendation algorithm for online teaching resources of vocal music based on graph neural network

[1] Yuly E. Sánchez, & Rodriguez, A. V. (2021). Digital educational resources in sciences for visual impaired students in times of covid - 19. Biophysical Journal, 120(3), 13a. Search in Google Scholar

[2] Valerie CamelMarie-Noelle MaillardNicolas DescharlesEven Le RouxMathieu CladiereIsabelle Billault. (2021). Open digital educational resources for self-training chemistry lab safety rules. Journal of Chemical Education, 98(1). Search in Google Scholar

[3] Chen, H., & Yang, W. (2021). Rank algorithm of web english educational resources based on fuzzy sets and rss. Journal of Intelligent and Fuzzy Systems(JUL.), 1-11. Search in Google Scholar

[4] Liu, Jinghua, Lin, Menglei, Yaojin, & Zhang, et al. (2016). An effective collaborative filtering algorithm based on user preference clustering. Applied Intelligence the International Journal of Artificial Intelligence Neural Networks & Complex Problem Solving Technologies. Search in Google Scholar

[5] Musto, C., Lops, P., Gemmis, M. D., & Semeraro, G. (2021). Context-aware graph-based recommendations exploiting personalized pagerank. Knowledge-Based Systems(3), 106806. Search in Google Scholar

[6] Yang, J. (2022). Personalized song recommendation system based on vocal characteristics. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[7] Fu, Z., Lian, T., Yao, Y., & Zheng, W. (2022). Mulsimnet: a multi-branch sub-interest matching network for personalized recommendation. Neurocomputing(Jul.21), 495. Search in Google Scholar

[8] Peng, B. (2021). Research and implementation of electronic commerce intelligent recommendation system based on the fuzzy rough set and improved cellular algorithm. Mathematical Problems in Engineering, 2021. Search in Google Scholar

[9] Lim, S., & Lee, J. (2013). An immersive augmented-reality-based e-learning system based on dynamic threshold marker method. Etri Journal, 35(6), 1048-1057. Search in Google Scholar

[10] Aher, S. B., & Lobo, L. M. R. J. (2013). Combination of machine learning algorithms for recommendation of courses in e-learning system based on historical data. Knowledge-Based Systems, 51(oct.), 1-14. Search in Google Scholar

[11] Vijaya, P., & Selvi, M. (2022). An approach using e-khool user log data for e-learning recommendation system. Journal of Information & Knowledge Management. Search in Google Scholar

[12] Zhang, L., Luo, T., Zhanga, F., & Wu, Y. (2018). A recommendation model based on deep neural network. IEEE Access, 6(99), 9454-9463. Search in Google Scholar

[13] Sheng, Z., Zhang, T., Zhang, Y., & Gao, S. (2023). Enhanced graph neural network for session-based recommendation. Expert Systems with Application. Search in Google Scholar

[14] Yan, Y., Li, C., Yu, Y., Li, X., & Zhao, Z. (2023). Osgnn: original graph and subgraph aggregated graph neural network. Expert Systems with Application. Search in Google Scholar

[15] Chang, Y., Chen, C., Hu, W., Zheng, Z., Zhou, X., & Chen, S. (2022). Megnn: meta-path extracted graph neural network for heterogeneous graph representation learning. Knowledge-Based Systems, 235, 107611-. Search in Google Scholar

[16] Bastani, H., Harsha, P., Perakis, G., & Singhvi, D. (2022). Learning personalized product recommendations with customer disengagement. Manufacturing and service operations management. Search in Google Scholar

[17] Song, Y., Lian, J., Sun, S., Huang, H., Li, Y., & Jin, H., et al. (2021). Show me the whole world: towards entire item space exploration for interactive personalized recommendations. Search in Google Scholar