Title: Intelligent recommendation method for digital teaching resources of online courses based on knowledge graph

Authors: Chao Xu

Addresses: Anyang Vocational and Technical College, Administration Office, Anyang, Henan, 455000, China

Abstract: In order to improve the effectiveness of recommending digital teaching resources for online courses, a research on intelligent recommendation methods for digital teaching resources for online courses based on knowledge graphs is carried out. Firstly, obtain digital teaching resources for online courses, construct a knowledge graph of digital teaching resources for online courses, and then mine interaction data between users to capture their preferences for digital teaching resources for online courses. Finally, comprehensively consider user interests, resource similarity, and knowledge connectivity to complete the intelligent recommendation process design for digital teaching resources for online courses. The experimental results show that the recommended accuracy of the proposed method is higher than 93.2%, the recall rate is higher than 94.1%, the recommended coverage can reach 94.6%, the user satisfaction rate is higher than 94.8%, which is better than the comparison method, and the application effect is good.

Keywords: knowledge graph; online courses; digital teaching resources; intelligent recommendation.

DOI: 10.1504/IJCEELL.2025.143802

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.1/2, pp.62 - 76

Received: 27 Dec 2023
Accepted: 09 Sep 2024
Published online: 07 Jan 2025 *

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