"Research on Accurate Recommendation of Learning Resources based on Gra" by SaiNan Wang, Bozhi 1 et al.

International Journal of Computer and Communication Technology

Article Title

Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms

Authors

SaiNan Wang, College of Elevator Engineering, Hunan Electric Polytechnic Xiangtan, ChinaFollow
Bozhi 1, College of Elevator Engineering, HuNan Electrical College OF Technology, Xiangtan,411101,ChinaFollow
Yuyi 3, Graphic Network Center, HuNan Electrical College OF Technology, Xiangtan,411101,ChinaFollow

Abstract

In response to the challenges of "learning confusion" and "information overload" in online learning, a personalized learning resource recommendation algorithm based on graph neural networks and convolution is proposed to address the cold start and data scarcity issues of existing traditional recommendation algorithms. Analyze the characteristics of the Knowledge graph of learners and curriculum resources in depth, use the graph Auto encoder to extract the auxiliary information and features in the Knowledge graph and establish the corresponding feature matrix, and use Convolutional neural network for classification and prediction. The experimental results show that this algorithm improves the performance of recommendation systems, improves learners' learning efficiency, and promotes personalized development.

Recommended Citation

Wang, SaiNan; 1, Bozhi; and 3, Yuyi (2022) "Research on Accurate Recommendation of Learning Resources based on Graph Neural Networks and Convolutional Algorithms," International Journal of Computer and Communication Technology: Vol. 8: Iss. 4, Article 6.
DOI: 10.47893/IJCCT.2022.1448
Available at: https://www.interscience.in/ijcct/vol8/iss4/6

DOI

10.47893/IJCCT.2022.1448

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