A personalized search model using online social network data based on a holonic multiagent system

Wang, Meijia; Liu, Qingshan; Lin, Yishuai

doi:10.23919/jcc.2020.02.015

Cited by 8 publications

(1 citation statement)

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“…International trade can also promote the exchange of different cultures and values, so that people of all countries can learn from different cultures and improve their own cultural level. Therefore, how to improve the bilingual teaching of international trade practice becomes particularly important [3][4].…”

Section: Introductionmentioning

confidence: 99%

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

Yin¹

2023

Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022)

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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, 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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Section: Introductionmentioning

confidence: 99%