Intelligent learning system based on personalized recommendation technology
With the continuous development of networks, web-based e-learning is changing the way people acquire knowledge. An increasing number of learners are eager to acquire more knowledge through personalized and intelligent means. Based on content recommendation and collaborative filtering recommendation...
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| Vydané v: | Neural computing & applications Ročník 31; číslo 9; s. 4455 - 4462 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Springer London
01.09.2019
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | With the continuous development of networks, web-based e-learning is changing the way people acquire knowledge. An increasing number of learners are eager to acquire more knowledge through personalized and intelligent means. Based on content recommendation and collaborative filtering recommendation algorithm, this paper proposes a hybrid recommendation algorithm which can improve the efficiency of traditional recommendation algorithm. The presented research introduces the whole process of user interest model and teaching resources model, which also designs and implements the personalized network teaching resources system prototype. Finally, in comparison with the traditional recommendation algorithm, the improved hybrid recommendation algorithm has more advantages in personalized intelligent educational resources recommendation system. |
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| AbstractList | With the continuous development of networks, web-based e-learning is changing the way people acquire knowledge. An increasing number of learners are eager to acquire more knowledge through personalized and intelligent means. Based on content recommendation and collaborative filtering recommendation algorithm, this paper proposes a hybrid recommendation algorithm which can improve the efficiency of traditional recommendation algorithm. The presented research introduces the whole process of user interest model and teaching resources model, which also designs and implements the personalized network teaching resources system prototype. Finally, in comparison with the traditional recommendation algorithm, the improved hybrid recommendation algorithm has more advantages in personalized intelligent educational resources recommendation system. |
| Author | Zhong, Zhaoman Li, Hui Li, Haining Cheng, Jiang Zhang, Shu |
| Author_xml | – sequence: 1 givenname: Hui surname: Li fullname: Li, Hui organization: Department of Computer Science, Huaihai Institute of Technology, Marine Resources Development Institute of Jiangsu – sequence: 2 givenname: Haining surname: Li fullname: Li, Haining email: 2002000051@hhit.edu.cn, lhaining@nccu.edu organization: Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory – sequence: 3 givenname: Shu surname: Zhang fullname: Zhang, Shu email: 2007000044@hhit.edu.cn organization: Business School, Huaihai Institute of Technology – sequence: 4 givenname: Zhaoman surname: Zhong fullname: Zhong, Zhaoman organization: Department of Computer Science, Huaihai Institute of Technology – sequence: 5 givenname: Jiang surname: Cheng fullname: Cheng, Jiang organization: Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory |
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| Cites_doi | 10.1016/j.future.2013.09.027 10.1016/j.eswa.2016.01.020 10.1016/j.jnca.2014.04.002 10.1016/j.future.2017.09.030 10.1016/j.engappai.2016.01.018 10.1109/TKDE.2014.2364027 10.1109/ACCESS.2017.2698142 10.1109/MCI.2014.2369894 10.1109/TMM.2015.2510333 10.1016/j.cageo.2015.12.015 10.2196/jmir.6971 10.1007/s11227-015-1518-5 |
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