Intelligent learning system based on personalized recommendation technology

• Published in: Neural computing & applications Vol. 31; no. 9; pp. 4455 - 4462
• Main Authors: Li, Hui, Li, Haining, Zhang, Shu, Zhong, Zhaoman, Cheng, Jiang
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2019; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s00521-018-3510-5

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.