Abstract
Aiming at the problem that the low matching degree between educational resource path planning and learners’ needs affects the success rate of recommendation, a distance education resource optimization recommendation method based on particle swarm optimization algorithm is proposed. Starting with the attributes of users and distance education resources, a portrait model is established to accurately describe the characteristics of users and resources. The characteristics of users and distance education resources in personalized learning resources are parameterized, the path of education resources is planned based on particle swarm optimization algorithm, and the learning path suitable for learners is formed by the reorganization and sorting of resources. A learner neighbor is established, and a recommendation model is established according to the difference matching degree between learners and learning resources. The experimental results show that when the number of educational resources is 1000, the average success rate of Distance Education Resource Recommendation Method Based on particle swarm optimization algorithm is 87.1%, which is 8.3% and 6.9% higher than that based on multivariate hybrid criteria fuzzy algorithm and multi-layer perceptron model. Therefore, the recommendation success rate of this method is relatively the highest, and can provide a set of learning resources that better match the characteristics of learners.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, L.: Design on college education information recommendation system based on fuzzy algorithm of multivariate mixed criteria. Mod. Electron. Tech. 43(4), 97–99 (2020)
Zhang, Y., Shen, H.: Research on label-based recommendation algorithms for self-help education resources. Mod. Inf. Technol. 3(12), 13–14 (2019)
Li, W., Wen, Y., Tang, L.: Research and implementation of personalized recommendation method for educational resources. Comput. Technol. Dev. 29(6), 18–22 (2019)
Cheng, J., Wang, H.: Adaptive algorithm recommendation and application of learning resources in English fragmented reading. Complexity 2021, 1–11 (2021)
He, Y., Xu, W.: Research on personalized educational resource recommendation algorithm based on high-dimensional tensor decomposition. Wireless Internet Technol. 18(10), 114–115 (2021)
Lu, N., Zhang, X., Ou, N.: Zero-shot learning by semantic autoencoder based on particle swarm optimization algorithm for attribute correlation. J. Electron. Inf. Technol. 43(4), 982–991 (2021)
Pan, F., An, Q., Diao, Q., et al.: Road extraction based on PSO different ratio deconvolution feature fusion. Trans. Beijing Inst. Technol. 40(6), 640–647 (2020)
She, X., Zhan, Q., Wu, C.: Multi-node information resource allocation recommendation algorithm based on collaborative filtering. Comput. Simul. 38(6), 419–423 (2021)
Wang, G., Yuan, H., Huang, X., Lu, M.: A recommendation algorithm for online learning resources based on improved collaborative filtering. Small Microcomput. Syst. 42(05), 940–945 (2021)
Lin, Y., Jiang, S.: Design on accurately personalized recommendation system for college students’ ideological and political education resources. Microcomput. Appl. 36(8), 55–58 (2020)
Xu, Z., Zhao, D.: Research on the construction of family education resource platform based on personalized recommendation algorithms. Comput. Knowl. Technol. 15(35), 238–239 (2019)
Funding
Science and technology project of Jiangxi Provincial Department of education: Research on Key Technologies of campus video monitoring system based on Internet of things (GJJ171481).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Gao, J., Huang, Y. (2022). Optimization and Recommendation Method of Distance Education Resources Based on Particle Swarm Optimization Algorithm. 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_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-21164-5_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21163-8
Online ISBN: 978-3-031-21164-5
eBook Packages: Computer ScienceComputer Science (R0)