Journal of Computer Technology and Software

Vol. 4 No. 12 (2025)
Articles

Personalized Learning Resource Recommendation via Enhanced Graph Neural Networks

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  • Rahul Sikka,
  • Ashok Aharon,
  • Zhoushun Zhang

Published 2025-12-30

How to Cite

Sikka, R., Aharon, A., & Zhang, Z. (2025). Personalized Learning Resource Recommendation via Enhanced Graph Neural Networks. Journal of Computer Technology and Software, 4(12). Retrieved from https://ashpress.org/index.php/jcts/article/view/240
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The intricate structure of knowledge points within learning resources and the diverse learning needs of users often lead to inconsistent recommendation outcomes. To mitigate this issue, this study introduces a personalized recommendation framework that integrates an improved graph neural network with user preference clustering. User behavior is examined from three dimensions-interaction patterns, self-directed learning awareness, and learning capability-to model individual learning preferences. A meta-data-driven representation of knowledge-point entities is incorporated into the graph neural network, enabling effective modeling of relationships among learning resources. After performing dual clustering on user preferences and resource entities, the framework identifies the correspondence between clusters to generate personalized recommendations. Experimental results show that the proposed approach achieves a precision of 60.96%, a recall of 65.42%, and an F-score of 62.57%, demonstrating its strong overall effectiveness.

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