A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the...

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Autor principal:	Zhihao Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/8e6cd588dd9742cabb94daeddeac8a07
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spelling	oai:doaj.org-article:8e6cd588dd9742cabb94daeddeac8a072021-11-08T02:36:09ZA Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology1875-919X10.1155/2021/9531111https://doaj.org/article/8e6cd588dd9742cabb94daeddeac8a072021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9531111https://doaj.org/toc/1875-919XThrough the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.Zhihao ZhangHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Zhihao Zhang A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology
description	Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.
format	article
author	Zhihao Zhang
author_facet	Zhihao Zhang
author_sort	Zhihao Zhang
title	A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology
title_short	A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology
title_full	A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology
title_fullStr	A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology
title_full_unstemmed	A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology
title_sort	method of recommending physical education network course resources based on collaborative filtering technology
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8e6cd588dd9742cabb94daeddeac8a07
work_keys_str_mv	AT zhihaozhang amethodofrecommendingphysicaleducationnetworkcourseresourcesbasedoncollaborativefilteringtechnology AT zhihaozhang methodofrecommendingphysicaleducationnetworkcourseresourcesbasedoncollaborativefilteringtechnology
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A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

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