Title: A method for pushing remote teaching materials based on knowledge graph and user profile

Authors: Linlin Hong; Meilian Jiang

Addresses: Nanchang Business College of Jiangxi Agricultural University, Gong Qingcheng, Jiangxi, 332020, China; Science and Technology Innovation Centre for Digital Rural Development in Jiujiang City, Gong Qingcheng, Jiangxi, 332020, China ' Nanchang Business College of Jiangxi Agricultural University, Gong Qingcheng, Jiangxi, 332020, China; Science and Technology Innovation Centre for Digital Rural Development in Jiujiang City, Gong Qingcheng, Jiangxi, 332020, China; Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia

Abstract: In order to improve the accuracy of teaching resource delivery and reduce delivery latency, this study proposes a remote teaching material delivery method based on knowledge graph and user profile. Firstly, the graph is divided into subsets to search for similar users, and a multi-layer RNN-based entity prediction model is used to complete the target platform user relationships. Secondly, comprehensive spatial and temporal data from multiple sources are collected to form personalised user profiles. Finally, a precise remote teaching resource delivery model based on support vector machine is constructed to achieve real-time delivery of personalised learning resources. The combination of knowledge graph and user profile enables precise delivery of learning resources through accurate user relationship completion and personalised feature analysis. Experimental results demonstrate that the proposed method consistently maintains accuracy above 96% in testing and exhibits significantly lower delivery latency compared to other methods.

Keywords: knowledge graph; user profile; remote teaching; teaching material push.

DOI: 10.1504/IJBIDM.2025.149084

International Journal of Business Intelligence and Data Mining, 2025 Vol.27 No.2/3/4, pp.299 - 314

Received: 31 Dec 2024
Accepted: 12 Jun 2025
Published online: 13 Oct 2025 *

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