Title: An optimisation and application of artificial intelligence models based on deep learning in personalised recommendation of English education

Authors: Peiqing Wang

Addresses: Basic Course Department, Henan Forestry Vocational College, Luoyang, Henan, China

Abstract: This study proposes an artificial intelligence model based on deep learning to optimise personalised recommendation methods for English education, addressing the problems of traditional recommendation methods neglecting student interests, English learning resource recommendations relying on subjective judgments or large mean square errors caused by popular learning paths. Firstly, based on the similarity measurement results of English educational resources, the K-means algorithm is used for resource clustering; secondly, utilising deep learning models to extract implicit features between users and English teaching resources, and using convolutional neural network models to predict English education resource ratings; finally, based on the predicted scores, personalised recommendations are provided using dynamic collaborative filtering algorithms. The experimental results show that the maximum mean square error of personalised recommendation using this method does not exceed 0.02, and the highest user satisfaction rate is 98%.

Keywords: deep learning; artificial intelligence models; English education; personalised recommendations.

DOI: 10.1504/IJCAT.2024.146134

International Journal of Computer Applications in Technology, 2024 Vol.75 No.2/3/4, pp.96 - 102

Received: 30 Aug 2024
Accepted: 02 Jan 2025
Published online: 07 May 2025 *

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