Title: Corpus-driven recommendation algorithm for English online autonomous learning resources

Authors: Chao Han

Addresses: Department of Foreign Language, Kaifeng University, Kaifeng, 475004, China

Abstract: In order to overcome the problems of the traditional recommendation algorithm of English online learning resources, such as low accuracy, poor convergence and low success rate of recommendation, this paper proposes a recommendation algorithm for online language autonomous learning resources driven by CORUS. Based on the learning vector quantisation (LQN) algorithm, the model of subject word generation based on vector corpus resources is established. In the parameter training of the classification model, the vector weight is normalised to complete the optimisation of the corpus resource classification LVQ subject model. According to the binary particle swarm optimisation algorithm, the personalised recommendation model of autonomous learning resources is implemented. Experimental results show that the proposed vector quantisation network algorithm has high convergence, and the recommended success rate is 99.5%. Therefore, the method proposed in this paper can effectively complete the recommendation of English online autonomous learning resources.

Keywords: corpus; English learning; learning resources; recommendation algorithm; binary particle swarm.

DOI: 10.1504/IJCEELL.2023.132389

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.4/5, pp.418 - 432

Received: 08 May 2021
Accepted: 09 Aug 2021

Published online: 19 Jul 2023 *

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