Knowledge-Aware Two-Tower Framework for Enhanced Course Recommendation: Integrating Knowledge Management for Precision and Efficiency in Online Education

Cuihua Xie, Yongguang Chen, Yuxuan Liu, Liyang Zhao, Qinglin Li, Mengyao Yang, Xin Zhang and Xinying Shi
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Cuihua Xie: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China†Research Center for Digital Innovation and Global Value, Chain Upgrading, Zhejiang Gongshang University, Hangzhou, P. R. China
Yongguang Chen: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China
Yuxuan Liu: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China
Liyang Zhao: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China
Qinglin Li: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China
Mengyao Yang: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China
Xin Zhang: ��Law School, Wenzhou University of Technology, Wenzhou, P. R. China
Xinying Shi: School of Economics and Management, Wenzhou University of Technology, Wenzhou, P. R. China

Journal of Information & Knowledge Management (JIKM), 2025, vol. 24, issue 05, 1-19

Abstract: The rapid expansion of online education has intensified the need for precise and efficient course recommendation systems to address challenges like information overload and personalised learning. This study bridges three critical research gaps: (1) the lack of integration between knowledge management and deep learning in educational recommendations, (2) the persistent trade-off between recommendation accuracy and computational efficiency and (3) insufficient modelling of pedagogical relationships in existing systems. We propose a Knowledge-Aware Two-tower Framework (KATT) that synergises Knowledge-Aware Networks (KAN) with a lightweight attention mechanism to achieve both high performance (Area Under the Curve (AUC): 0.8645, Accuracy (ACC): 78.13%) and interpretability. Evaluations on the MoocCube dataset demonstrate KATT’s superiority over six state-of-the-art baselines, particularly in cold-start scenarios (15% improvement) and recommendation explainability. The framework’s balanced approach to pedagogical relevance and system efficiency offers a novel solution for scalable personalised education.

Keywords: Online education recommendation; Knowledge-Aware Networks (KAN); two-tower architecture; knowledge management; personalised learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219649225500558

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