Abstract
To overcome the issues of high error rate in extracting interest preferences, low recommendation accuracy, and long recommendation task completion time in traditional personalized recommendation algorithms for online learning resources, a personalized recommendation method for innovation and entrepreneurship online learning resources for college students is proposed. The Flume framework is used to construct a data collection framework for capturing college students’ innovation and entrepreneurship online learning behavior, resulting in the collection of learning behavior data. The collected learning behavior data is then used to represent the features of innovation and entrepreneurship online learning resources, enabling the extraction of college students’ interest preferences. Improvements are made to the traditional collaborative filtering algorithm by incorporating the features of college students’ interest preferences, resulting in a personalized recommendation of innovation and entrepreneurship online learning resources for college students. Experimental results demonstrate that this method achieves a higher accuracy in extracting college students’ interest preferences, improves recommendation accuracy, reduces recommendation task completion time, and produces reliable recommendation results.
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
Similar content being viewed by others
References
Yuan, F., Hu, D.: Research on the application of big data technology in college students’ innovation and entrepreneurship guidance service. J. Phys: Conf. Ser. 1883(1), 012171–012179 (2021)
Zhou, Q.: Research on the problems and countermeasures of the cultivation of adult college students’ innovation and entrepreneurship ability in the internet era. Open Access Lib. J. 8(7), 1–12 (2021)
Wu, B., Ye, S., Yang, Z.: The influencing factors of innovation and entrepreneurship intention of college students based on AHP. Math. Probl. Eng. 22(6), 125–139 (2022)
Nie, L.S.: Personalized recommendation of learning resources based on behavior analysis. Comput. Technol. Develop. 30(7), 34–37, 41 (2020)
Liu, F., Tian, F., Li, X., et al.: A collaborative filtering recommendation method for online learning resources incorporating the learner model. CAAI Trans. Intell. Syst. 16(6), 1117–1125 (2021)
Chen, X., Deng, H.: Research on personalized recommendation methods for online video learning resources. Appl. Sci. 11(2), 804 (2021)
Xu, Y., Chen, T.: The design of personalized learning resource recommendation system for ideological and political courses. Int. J. Reliab. Qual. Saf. Eng. 30(1), 1–10 (2023)
Li, H., Zhong, Z., Shi, J., et al.: Multi-objective optimization-based recommendation for massive online learning resources. IEEE Sens. J. 21(22), 25274–25281 (2021)
Fan, J., Jiang, Y., Liu, Y., et al.: Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis. Internet Res. Electron. Netw. Appl. Policy 32(2), 588–605 (2022)
Hu, X., Wang, Y., Chen, Q.B., et al.: Research on personalized learning based on collaborative filtering method. J. Phys: Conf. Ser. 1757(1), 012050–012063 (2021)
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
Gao, D. (2024). Research on Personalized Recommendation of Online Learning Resources for Innovation and Entrepreneurship Among College Students. In: Gui, G., Li, Y., Lin, Y. (eds) e-Learning, e-Education, and Online Training. eLEOT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-51471-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-51471-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51470-8
Online ISBN: 978-3-031-51471-5
eBook Packages: Computer ScienceComputer Science (R0)