WEB Content Recommendation Algorithms in Distance Education
Authors: 
Lin Ma, Lulu Ma, Yonglan Wu, Feng Luo, Xingtian CaoAuthors Info & Claims
Lin Ma, Lulu Ma, Yonglan Wu, Feng Luo, Xingtian CaoAuthors Info & ClaimsPages 235 - 241
Abstract
In the realm of modern distance education, the application of web content recommendation algorithms has emerged as a pivotal means to enhance learning efficiency and personalize the educational experience. This paper aims to explore the various technologies of web-based personalized recommendation within the context of remote education and their impact on learning outcomes. Through the analysis of recommendation methods based on content, rules, collaborative filtering, demographic information, and association rules, this study systematically discusses the design and implementation of personalized recommendation algorithms in distance education. Notably, a detailed analysis of collaborative filtering algorithms, including user-based and item-based collaborative filtering as well as model-based approaches, provides both theoretical and technical support for remote education. Experimental results indicate that personalized recommendation algorithms not only significantly improve the matching of learning resources but also effectively enhance students' satisfaction and learning outcomes. This research offers valuable insights for the design and optimization of future distance education systems.
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Index Terms
- WEB Content Recommendation Algorithms in Distance Education
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Published In
August 2024
443 pages
ISBN:9798400710353
DOI:10.1145/3697467
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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Association for Computing Machinery
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Published: 08 November 2024
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IoTML 2024
IoTML 2024: 2024 4th International Conference on Internet of Things and Machine Learning
August 9 - 11, 2024
Nanchang, China
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