Education Resource Recommendation Based on Graph Neural Network and Multi-Subject Rating
In existing education resource recommendation systems,an increasing number of novel algorithms and models have been developed,consistently considering user preference analysis and content relevance calculation.However,these systems often neglect the influence of education objective plans from teachers and practical guidance from corporate mentors.Therefore,this study proposes a education resource recommendation method based on Graph Neural Network(GNN)and multi-subject rating,which considers ratings from multiple subjects,including students,teachers,and corporate mentors,further solving the few-shot problem caused by the multi-subject rating.In this method,education resources and knowledge points are first put into a heterogeneous graph and then their feature vectors are represented via heterogeneous graph embedding to calculate the relevance between them.Subsequently,a multi-subject rating mechanism is designed,in which fine-grained rating indicators are defined for different subjects and the corresponding rating values from different subjects are estimated via the GNN-based Few-Shot Learning(GNN-FSL)model.Finally,an attention mechanism is utilized to integrate the influence of rating values from different subjects and the relevance of education resources and knowledge points on the recommendation results.The experimental results indicate that the proposed method not only improves rating accuracy in few-shot training but also facilitates multi-subject rating and fine-grained rating indicators to improve both recommendation accuracy and student grades.
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