Year: 2025
Type: conference-paper
Abstract: The rise of online education platforms has brought about massive educational resources, as well as problems such as information overload and learning disorientation. Personalized recommendation system can recommend appropriate learning resources for learners according to their interests and learning goals, thus improving learning efficiency and learning experience. In recent years, graph neural networks (GNNs) have made significant progress in graph-structured data representation learning, providing new ideas for personalized educational resources recommendation. In this paper, we propose a personalized educational resource recommendation method based on graph neural network. By constructing a learner-resource interaction graph and utilizing a multimodal fusion model combining graph convolutional network (GCN) and graph attention network (GAT), the method in this paper is able to effectively capture the complex relationship between learners and resources and generate high-quality personalized recommendation results.
Authors: Xiangting Gao, Xingying Han, Mario Di Nardo
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4412923161