Bilingual teaching resource recommendation method for international trade practice based on knowledge graph and neural network

Ping Yin

doi:10.1117/12.2671654

29 March 2023 Bilingual teaching resource recommendation method for international trade practice based on knowledge graph and neural network

Ping Yin

Author Affiliations +

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 125942H (2023) https://doi.org/10.1117/12.2671654
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

Aiming at the problem that the traditional bilingual teaching resource recommendation technology does not take into account the differences of different learners' learning style, knowledge level and learning mode, and does not take into account the individuation. This paper proposes a bilingual teaching resource recommendation method for international trade practice based on knowledge graph and neural network. Firstly, we correlate the content to be learned with the resources, use the existing knowledge mapping technology, and then use Word2Vec to quantify its behavior and characteristics, and the recommendation model is embedded. Finally, BiLSTM and Attention are used to extract features, dig learners' implicit feedback information, and achieve the purpose of personalized recommendation for learners. Experimental results show that the proposed model is more effective than the traditional recommendation algorithm, and effectively improves the effect of bilingual teaching resources recommendation.

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Ping Yin "Bilingual teaching resource recommendation method for international trade practice based on knowledge graph and neural network", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 125942H (29 March 2023); https://doi.org/10.1117/12.2671654

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KEYWORDS

Neural networks

Data modeling

Education and training

Performance modeling

Semantics

Cognitive modeling

Design and modelling

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