Abstract
Aiming at the problem of high similarity in online education courseware for painting, which leads to poor recommendation effectiveness. In order to optimize the recommendation effect of online education courseware for painting and improve the effectiveness of online education for painting, a personalized recommendation method for online education courseware for painting based on hyper heuristic algorithm is proposed. This paper introduces the calculation of keyword weight of personalized recommendation, the calculation of similarity of online painting education courseware, the calculation of user similarity, and the Committed step of the calculation of similarity between users and online painting education courseware. The Ant colony optimization algorithms in the super heuristic algorithm is used to design the personalized recommendation process and complete the theoretical research on personalized recommendation of online painting education courseware. The experimental results indicate that this method the average accuracy of this method is 91% with interest and 86% with no interest. When the number of people in the same period increases to 1000, the growth rate of system throughput slows down and the growth rate is relatively small. This method can recommend high-quality online education courseware for painting, which helps improve the learning experience and effectiveness of users for online education courseware for painting.
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
Merritt, E.G., Powell, R.B., Stern, M.J., Frensley, B.T.: A systematic literature review to identify evidence-based principles to improve online environmental education. Environ. Educ. Res. 28(5), 674–694 (2022)
Katz, D., Huggins-Manley, A.C., Leite, W.: Personalized online learning, test fairness, and educational measurement: considering differential content exposure prior to a high stakes end of course exam. Appl. Meas. Educ. 35(1), 1–16 (2022)
Li, X.R., Liang, H.W., Feng, J.Y., Xiao, J.P., Peng, W.F.: Design of personalized learning resource recommendation system for online education platform. Comput. Technol. Dev. 31(2), 143–149 (2021)
Zhang, Y.B., Zhao, J.L.: Personalized recommendation method of network information integrating LDA and attention. Comput. Simul. 39(12), 528–532 (2022)
Zhang, J.D., Jiang, L.P.: Research on recommendation of online education service based on social media gene mapping. Inf. Stud. Theory Appl. 44(8), 131–138 (2021)
Yu, H., Ji, Y., Li, Q.: Student sentiment classification model based on GRU neural network and TF-IDF algorithm. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 40(2), 2301–2311 (2021)
Shen, X., Junwei, D.U., Gong, D., Yao, X.: Developer cooperation relationship and attribute similarity based community detection in software ecosystem. Chin. J. Electron. 32(1), 39–50 (2023)
Rao, K.S., Sridhar, M.: A tabu search algorithm for general threshold visual cryptography schemes. Ingénierie des Systèmes D Information 26(3), 329–335 (2021)
Alves, D., Oliveira, D., Andrade, E., Nogueira, B.: GPU-brkga: a GPU accelerated library for optimization using the biased random-key genetic algorithm. Lat. Am. Trans. 20(1), 14–21 (2022)
Pramanik, S., Goswami, A.: Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm. Appl. Intell. Int. J. Artif. Intell. Neur. Netw. Complex Probl. Solving Technol. 52(8), 8839–8835 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yu, R., Tan, B. (2024). A Personalized Recommendation Method for Online Painting Education Courseware Based on Hyperheuristic Algorithm. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-031-50549-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-50549-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50548-5
Online ISBN: 978-3-031-50549-2
eBook Packages: Computer ScienceComputer Science (R0)