Title: Personalised recommendation method of online education learning resources based on collaborative filtering algorithm

Authors: Yang Yang

Addresses: Center of Education Technology, Henan University of Economics and Law, Zhengzhou, 450046, China; School of Education, Henan University, Zhengzhou, 450046, China

Abstract: In this paper, a personalised recommendation method for online education learning resources based on collaborative filtering algorithm is proposed. Firstly, the traditional crawler technology is used to collect resource data, and the implicit crawler technology is introduced to obtain key resources. Then, the automatic encoder extracts the nonlinear features, combines the expected risk minimisation and sample error, defines the empirical risk classification resource data, and completes the pre-treatment of resource data. Finally, a collaborative filtering algorithm is introduced to obtain learners' individual needs through forgetting factor optimisation, and a collaborative filtering recommendation model is designed. The results show that the recommended error of the proposed method is 0.10%, the recommended time is less than two seconds, and the work efficiency is over 95%, indicating that the method can improve the accuracy and efficiency of resource recommendation.

Keywords: collaborative filtering algorithm; online education; learning resources; personalised recommendation.

DOI: 10.1504/IJRIS.2025.148026

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.4, pp.217 - 225

Received: 13 Apr 2023
Accepted: 23 May 2023
Published online: 15 Aug 2025 *

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