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Research on Intelligent English Education Based on the Short Video Recommendation Algorithm

In order to solve the problems of English education in the form of a short video, a research method of English intelligent education based on a short video recommendation algorithm was proposed. The recommendation system is a branch of artificial intelligence data mining, which improves the efficien...

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Detalles Bibliográficos
Autor principal: Li, Xiaoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348862/
https://www.ncbi.nlm.nih.gov/pubmed/37455770
http://dx.doi.org/10.1155/2023/2317589
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author Li, Xiaoyu
author_facet Li, Xiaoyu
author_sort Li, Xiaoyu
collection PubMed
description In order to solve the problems of English education in the form of a short video, a research method of English intelligent education based on a short video recommendation algorithm was proposed. The recommendation system is a branch of artificial intelligence data mining, which improves the efficiency of short videos for English learning. The density ratio of users and video scoring matrix was 1000000/(1030 × 9394) = 10.3%. The dataset was a relatively sparse matrix. The original dataset was randomly divided into the training set and the test set, accounting for 80% and 20%, respectively. Then, the results of the short video recommendation algorithm were elaborated based on time weighting. Finally, the intelligent initial question bank of English intelligent education based on a short video recommendation algorithm was elaborated, which provided a guarantee for the promotion of short videos in English education.
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spelling pubmed-103488622023-07-15 Research on Intelligent English Education Based on the Short Video Recommendation Algorithm Li, Xiaoyu Comput Intell Neurosci Research Article In order to solve the problems of English education in the form of a short video, a research method of English intelligent education based on a short video recommendation algorithm was proposed. The recommendation system is a branch of artificial intelligence data mining, which improves the efficiency of short videos for English learning. The density ratio of users and video scoring matrix was 1000000/(1030 × 9394) = 10.3%. The dataset was a relatively sparse matrix. The original dataset was randomly divided into the training set and the test set, accounting for 80% and 20%, respectively. Then, the results of the short video recommendation algorithm were elaborated based on time weighting. Finally, the intelligent initial question bank of English intelligent education based on a short video recommendation algorithm was elaborated, which provided a guarantee for the promotion of short videos in English education. Hindawi 2023-07-07 /pmc/articles/PMC10348862/ /pubmed/37455770 http://dx.doi.org/10.1155/2023/2317589 Text en Copyright © 2023 Xiaoyu Li. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Xiaoyu
Research on Intelligent English Education Based on the Short Video Recommendation Algorithm
title Research on Intelligent English Education Based on the Short Video Recommendation Algorithm
title_full Research on Intelligent English Education Based on the Short Video Recommendation Algorithm
title_fullStr Research on Intelligent English Education Based on the Short Video Recommendation Algorithm
title_full_unstemmed Research on Intelligent English Education Based on the Short Video Recommendation Algorithm
title_short Research on Intelligent English Education Based on the Short Video Recommendation Algorithm
title_sort research on intelligent english education based on the short video recommendation algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348862/
https://www.ncbi.nlm.nih.gov/pubmed/37455770
http://dx.doi.org/10.1155/2023/2317589
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