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Knowledge graph enhancement exercise recommendation algorithm based on multi-task learning
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AI
DOI：10.1007/s10489-025-07059-z.png)
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Abstract
En 中文
In the realm of online education, it is a fundamental task to assess the mastery of knowledge points by students and to customize personalized exercises for them. However, recommending suitable exercises for students poses a challenge due to the varying levels of student knowledge and the extensive exercise question bank. Current methodologies utilize collaborative filtering to recommend exercises, which lack consideration of changes in student knowledge, making it difficult to capture student behavior and utilize the relationship information between students and exercises. Therefore, this paper proposes an end-to-end knowledge graph (KG) enhanced exercise recommendation algorithm for multi-task learning (KERM). By incorporating a feature sharing unit, the recommendation task and knowledge graph embedding (KGE) task automatically share additional feature information with each other, thereby enhancing the accuracy of exercise recommendation. Firstly, a self-adaptive adjustment factor has been designed that continuously monitors the changes in knowledge state of the students in the recommendation task. Subsequently, a cleverly designed single multi-level feature interaction is implemented in the KGE task where single-level feature interactions extract detailed entity information and capture rich interaction details within entity neighborhoods, multi-level feature interactions aggregate multiple single-level interactions to expand receptive field coverage and improve quality of feature interaction. Finally, a feature sharing unit is designed to model high-order interactions between student and exercise features by automatically sharing additional information between both tasks to help prevent overfitting while improving robustness. To verify the effectiveness of KERM, extensive experiments are conducted on four real datasets yielding average prediction accuracy by 4.2%, 5.6%, 4.1%, and 4.9% respectively.
Keywords:
Exercise recommendation
Knowledge graph
Knowledge graph embedding
Multi-task learning
Multi-level feature interaction
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