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Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Sisi Li, Bo Yang
Copyright: © 2023 |Volume: 16 |Issue: 3 |Pages: 15
ISSN: 1935-570X|EISSN: 1935-5718|EISBN13: 9781668489529|DOI: 10.4018/IJITSA.321133
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MLA

Li, Sisi, and Bo Yang. "Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments." IJITSA vol.16, no.3 2023: pp.1-15. http://doi.org/10.4018/IJITSA.321133

APA

Li, S. & Yang, B. (2023). Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments. International Journal of Information Technologies and Systems Approach (IJITSA), 16(3), 1-15. http://doi.org/10.4018/IJITSA.321133

Chicago

Li, Sisi, and Bo Yang. "Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments," International Journal of Information Technologies and Systems Approach (IJITSA) 16, no.3: 1-15. http://doi.org/10.4018/IJITSA.321133

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Abstract

The goal of this article is to analyze the problem of low computational efficiency and propagation error rate in entity recognition and relation extraction. This paper proposes a personalized education resource recommendation algorithm framework XMAMBLSTM based on deep learning in an intelligent education robot environment. XMAMBLSTM uses XLNet to assign word vectors to text sequences, employs a Multi-Bi-LSTM layer to represent complex information of word vectors, and combines a multi-headed attention layer to realize weight distribution of each word vector. The experimental results show that compared with the traditional collaborative filtering algorithm, the comprehensive evaluation indexes of the proposed method, based on the intelligent education robot environment on the two platforms, are higher than 5.05% and 17.3%, respectively.