Title: Research on personalised recommendation method for English online course resources based on hybrid differential evolution algorithm

Authors: Wei Zhu

Addresses: School of Foreign Languages and Cultures, Nanjing Normal University (Taizhou College), Taizhou, Nanjing, China

Abstract: In order to improve personalised satisfaction and recommendation accuracy of English resources, and effectively reduce recommendation time, this article proposes a personalised recommendation method for English online course resources based on hybrid differential evolution algorithm. Firstly, collect English resource data; secondly, matrix decomposition technology is introduced to determine the user's interest information. Then, select the naive Bayesian algorithm to classify the resources. Finally, the fitness value of the individual is calculated, and the personalised recommendation function is designed using the hybrid differential evolution algorithm. The recommendation function is solved through differential mutation, and the final recommendation result is obtained. The results show that the recommendation satisfaction of this method can reach up to 99.5%, the accuracy can reach 99.5% and the recommendation time always does not exceed 4 seconds, effectively improving the personalised precision recommendation effect.

Keywords: differential evolution algorithm; matrix decomposition; Naive Bayes English online courses; personalised recommendations.

DOI: 10.1504/IJCAT.2024.141363

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.73 - 79

Received: 17 Oct 2023
Accepted: 13 Feb 2024
Published online: 09 Sep 2024 *

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