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Research on digital English teaching materials recommendation based on improved machine learning

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  • Miao Ma

Abstract

In order to overcome the problems of low accuracy, time-consuming and low user satisfaction in traditional methods, a digital English teaching materials recommendation method based on improved machine learning is proposed. Firstly, use web crawlers to obtain the data of digital English teaching platform, and use Word2vec model data for training to obtain the data feature vector. Secondly, K-means algorithm is used to cluster users according to feature vectors, and multi-Markov chains are used to predict user interest. Finally, the decision tree algorithm in machine learning is improved on the gradient boosting framework, and the digital English teaching materials are recommended by using the improved algorithm and the user interest prediction results. The experimental results show that the accuracy of this method is more than 96%, the average time of digital English teaching materials recommendation is 76.1 ms, and the average user satisfaction is 96.6.

Suggested Citation

  • Miao Ma, 2025. "Research on digital English teaching materials recommendation based on improved machine learning," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 24(1/2), pages 78-91.
    • Handle: RePEc:ids:ijitma:v:24:y:2025:i:1/2:p:78-91
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