Abstract
The rapid growth of network teaching resources brings more learning opportunities for people, but also makes it more and more expensive for users to find the resources they need, and users often get lost in a large number of resources. In view of the problem of low coverage of existing teaching resource recommendation methods, this paper studies a resource recommendation method of online ideological and political courses based on content correlation. In this method, word frequency-inverse document matrix (TF-IDF) is used to mine the keywords of ideological and political teaching resources, and the user interest description matrix is established, and the content correlation degree is calculated by cosine similarity. Top-k online teaching resources of ideological and political courses in universities that are most similar to the target customer’s interest description vector are selected based on the correlation degree and recommended to the user. The results show that the accuracy rate and coverage rate of the proposed method are higher than those of the collaborative filtering and utility-based methods, indicating that the proposed method has better performance.
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References
Cui, Z., et al.: Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Trans. Serv. Comput. 13(4), 685–695 (2020)
Chiu, M.C., Huang, J.H., Gupta, S., Akman, G.: Developing a personalized recommendation system in a smart product service system based on unsupervised learning model. Comput. Ind. 128(10), 103421 (2021)
Safran, M., Che, D.: Efficient learning-based recommendation algorithms for Top-N tasks and Top-N workers in large-scale crowdsourcing systems. ACM Trans. Inform. Syst. 371, 21–246 (2019)
Yu, X.B., Zhan, Q.H., Wu, C.X.: Multi-node information resource allocation recommendation algorithm based on collaborative filtering. Comput. Simul. 38(06), 419–423 (2021)
Pan, H., Zhang, Z.: Research on context-awareness mobile tourism e-commerce personalized recommendation model. J. Signal Process. Syst. 93(3), 1–8 (2021)
Huang, Y., Huang, W.J., Xiang, X.L., Yan, J.J.: An empirical study of personalized advertising recommendation based on DBSCAN clustering of sina weibo user-generated content. Procedia Comput. Sci. 183(8), 303–310 (2021)
Hu, Z., Wang, J., Yan, Y., Zhao, P., Chen, J., Huang, J.: Neural graph personalized ranking for Top-N recommendation. Knowl.-Based Syst. 213(8), 106426 (2020)
Sun, Z., Anbarasan, M., Praveen, K.D.: Design of online intelligent English teaching platform based on artificial intelligence techniques. Comput. Intell. 37(3), 1166–1180 (2021)
Li, N., Chen, X., Subramani, S., Kadry, S.N.: Improved fuzzy-assisted multimedia-assistive technology for engineering education. Comput. Appl. Eng. Educ. 29(2), 453–464 (2021)
Gao, J., et al.: Optimization analysis and implementation of online wisdom teaching mode in cloud classroom based on data mining and processing. Int. J. Emerg. Technol. Learn. (iJET) 16(1), 205–218 (2021)
Funding
Provincial level research project on teaching reform of colleges and universities in Jiangxi Province: Research on the dilemma and breakthrough of the construction of Ideological and Political Theory Teachers in private colleges and universities in Jiangxi Province (Subject No.: JXJG-21-35-2).
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gu, H., Chen, L. (2022). An Auxiliary Recommendation Method for Online Teaching Resources of Ideological and Political Courses in Colleges Based on Content Association. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-21164-5_1
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DOI: https://doi.org/10.1007/978-3-031-21164-5_1
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