Comparison of Recommendation Models Based on GraphSAGE, Heterogeneous Graph Transformer and Heterogeneous Graph Attention Network for Educational Recommendation in Heterogeneous Knowledge Graphs
Ramazanova V. Serikbayeva S. Sambetbayeva M. Yerimbetova A.
2025Institute of Electrical and Electronics Engineers Inc.
International Conference on Computer Science and Engineering, UBMK
2025Issue 2025628 - 633 pp.
The article considers the problem of constructing personalized educational recommendations based on a heterogeneous knowledge graph that combines data from university curricula, vacancies, and online courses. To rank courses by relevance to the users competence, an approach based on heterogenous graph neural networks is proposed. The experiments were conducted on a heterogeneous graph that includes different types of nodes and edges. In the experimental study, the quality of the models was assessed using different metrics. The results showed the superiority of the recommendation model with the HGTConv layer on the link task weight prediction, especially in terms of ranking quality metrics, which indicates its suitability for building recommendations in educational systems. The presented approach can be used to develop adaptive recommendations in education focused on the users career goals.
course ranking , graph neural networks , GraphSAGE , Heterogeneous Graph Attention Network , Heterogeneous Graph Transformer , heterogeneous knowledge graph , link regression , link weight prediction , meta-paths , meta-relations , personalized recommendations , skill embeddings
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Institute of Information and Computational Technologies of the Committee of Science, Ministry of Science and Higher Education of the Republic of Kazakhstan, Astana, Kazakhstan
L.N. Gumilyov Eurasian National University, Department of Information Systems, Astana, Kazakhstan
Institute of Information and Computing Technologies, Almaty, Kazakhstan
Institute of Information and Computational Technologies of the Committee of Science
L.N. Gumilyov Eurasian National University
Institute of Information and Computing Technologies
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026