Title: Hybrid recommendation of English online learning materials based on self-supervised learning

Authors: Kelu Wang

Addresses: School of Foreign Languages, Leshan Normal University, Leshan, Sichuan, China

Abstract: To overcome the low recall rate, precision and satisfaction of traditional recommendation methods, a hybrid recommendation method of English online learning materials based on self-supervised learning is proposed. Firstly, English learning knowledge point information is crawled to obtain user preferences for English online learning materials. Then, a multi-granularity node deletion strategy is used to enhance user representation and a self-supervised learning trainer is constructed to train English online learning materials and user preferences. Finally, a recommendation function for material design based on group representation learning is developed, and an attention mechanism is used to calculate the weighted sum of member preference representations, solving the recommendation function to obtain the final recommendation results. The experimental results demonstrate that when the English learning material is 500 GB, the recall rate of this method is 99.2%, the accuracy rate reaches 99.9% and the recommendation satisfaction can reach 99.8%.

Keywords: self-supervised learning; English online learning materials; hybrid recommendation; multi-granularity node deletion strategy; attention mechanism.

DOI: 10.1504/IJCAT.2024.141360

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.44 - 51

Received: 20 Oct 2023
Accepted: 13 Feb 2024
Published online: 09 Sep 2024 *

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