Curriculum–Vacancy–Course Recommendation Model Based on Knowledge Graphs, Sentence Transformers, and Graph Neural Networks

Ramazanova V. Sambetbayeva M. Serikbayeva S. Yerimbetova A. Lamasheva Z. Sadirmekova Z. Kalman G.
August 2025 Multidisciplinary Digital Publishing Institute (MDPI)

Technologies
2025 #13 Issue 8

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals.

course recommendation , domain integration , graph neural network (GNN) , graph sample and aggregate network (GraphSAGE) , heterogeneous graph attention network (HAN) , heterogeneous graph transformer (HGT) , heterogeneous knowledge graph , link regression , link weight prediction , meta-paths , meta-relations , ontology , personalized recommendations , skill embeddings

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Faculty of Information Technology, L. N. Gumilyov Eurasian National University, Astana, 010000, Kazakhstan
Institute of Information and Computing Technologies, Almaty, 050000, Kazakhstan
Department of Information and Communication Technologies, Shokan Ualikhanov Kokshetau University, Kokshetau, 020000, Kazakhstan

Faculty of Information Technology
Institute of Information and Computing Technologies
Department of Information and Communication Technologies

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