Research on Personalized Recommendation Methods for Online Video Learning Resources
It is not easy to find learning materials of interest quickly in the vast amount of online learning materials. The purpose of this study is to find students’ interests according to their learning behaviors in the network and to recommend related video learning materials. For the students who do not...
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doaj-670203082e5f4db6b03b0a744ba871d72021-01-16T00:06:01ZengMDPI AGApplied Sciences2076-34172021-01-011180480410.3390/app11020804Research on Personalized Recommendation Methods for Online Video Learning ResourcesXiaojuan Chen0Huiwen Deng1Business College, Southwest University, Chongqing 402460, ChinaSchool of Computer and Information Science, Southwest University, Chongqing 400715, ChinaIt is not easy to find learning materials of interest quickly in the vast amount of online learning materials. The purpose of this study is to find students’ interests according to their learning behaviors in the network and to recommend related video learning materials. For the students who do not leave an evaluation record in the learning platform, the association rule algorithm in data mining is used to find out the videos that students are interested in and recommend them. For the students who have evaluation records in the platform, we use the collaborative filtering algorithm based on items in machine learning, and use the Pearson correlation coefficient method to find highly similar video materials, and then recommend the learning materials they are interested in. The two methods are used in different situations, and all students in the learning platform can get recommendation. Through the application, our methods can reduce the data search time, improve the stickiness of the platform, solve the problem of information overload, and meet the personalized needs of the learners.https://www.mdpi.com/2076-3417/11/2/804personalized recommendationdata miningCollaborative Filtering Algorithme-Learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaojuan Chen Huiwen Deng |
spellingShingle |
Xiaojuan Chen Huiwen Deng Research on Personalized Recommendation Methods for Online Video Learning Resources Applied Sciences personalized recommendation data mining Collaborative Filtering Algorithm e-Learning |
author_facet |
Xiaojuan Chen Huiwen Deng |
author_sort |
Xiaojuan Chen |
title |
Research on Personalized Recommendation Methods for Online Video Learning Resources |
title_short |
Research on Personalized Recommendation Methods for Online Video Learning Resources |
title_full |
Research on Personalized Recommendation Methods for Online Video Learning Resources |
title_fullStr |
Research on Personalized Recommendation Methods for Online Video Learning Resources |
title_full_unstemmed |
Research on Personalized Recommendation Methods for Online Video Learning Resources |
title_sort |
research on personalized recommendation methods for online video learning resources |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-01-01 |
description |
It is not easy to find learning materials of interest quickly in the vast amount of online learning materials. The purpose of this study is to find students’ interests according to their learning behaviors in the network and to recommend related video learning materials. For the students who do not leave an evaluation record in the learning platform, the association rule algorithm in data mining is used to find out the videos that students are interested in and recommend them. For the students who have evaluation records in the platform, we use the collaborative filtering algorithm based on items in machine learning, and use the Pearson correlation coefficient method to find highly similar video materials, and then recommend the learning materials they are interested in. The two methods are used in different situations, and all students in the learning platform can get recommendation. Through the application, our methods can reduce the data search time, improve the stickiness of the platform, solve the problem of information overload, and meet the personalized needs of the learners. |
topic |
personalized recommendation data mining Collaborative Filtering Algorithm e-Learning |
url |
https://www.mdpi.com/2076-3417/11/2/804 |
work_keys_str_mv |
AT xiaojuanchen researchonpersonalizedrecommendationmethodsforonlinevideolearningresources AT huiwendeng researchonpersonalizedrecommendationmethodsforonlinevideolearningresources |
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1724336127919259648 |