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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Vydáno v:Neural computing & applications Ročník 31; číslo 9; s. 4455 - 4462
Hlavní autoři: Li, Hui, Li, Haining, Zhang, Shu, Zhong, Zhaoman, Cheng, Jiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.09.2019
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Distance learning
Image Processing and Computer Vision
Probability and Statistics in Computer Science
Recommender systems
S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
SVM
Collaborative filtering
Smart education
Learning resource
ISSN:0941-0643, 1433-3058
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3510-5