Abstract
With the development of online courses and MOOCs, the traditional course learning recommendation platform can no longer meets the individual needs of learners at different levels. After careful analysis of the current recommendation methods, the grid environment is proposed based on the grid environment. A deep learning-based curriculum learning personalized recommendation system model, which collects basic data, professional basic data, and curriculum basic data for a large number of students, establishes a personalized mathematical model for curriculum recommendation, and trains learning models and student data. According to the results, the training parameters are continuously adjusted to accurately recommend the course learning resources for students, thereby reducing resource processing and retrieval time, and improving students’ efficiency in course learning.
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Acknowledgements
This work was supported by the project of Nature Scientific Foundation of Heilongjiang Province (F2016038); at the same time, this paper is also supported by the planning of the Education Department of Heilongjiang Province (GJC1316189).
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Liu, F., Guo, W. (2020). Research and Design of Personalized Recommendation System Model for Course Learning Based on Deep Learning in Grid Environment. In: Yang, CT., Pei, Y., Chang, JW. (eds) Innovative Computing. Lecture Notes in Electrical Engineering, vol 675. Springer, Singapore. https://doi.org/10.1007/978-981-15-5959-4_208
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DOI: https://doi.org/10.1007/978-981-15-5959-4_208
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