research-article

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Authors:

Zhijun ZhangAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2021

https://doi.org/10.1155/2021/9590502

Published: 01 January 2021 Publication History

Abstract

Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners’ enthusiasm and give full play to different learners’ learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.

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Cited By

Li SZhao YGuo LRen MLi JZhang LLi K(2024)Quantification and prediction of engagementInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10353661:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103536
Cheng LShi YLi LYu HWang XYan Z(2023)KLECA: knowledge-level-evolution and category-aware personalized knowledge recommendationKnowledge and Information Systems10.1007/s10115-022-01789-z65:3(1045-1065)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10115-022-01789-z
Huang SLiu QChen JHu XLiu ZLuo W(2022)A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online LearningArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium10.1007/978-3-031-11647-6_36(208-212)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-11647-6_36

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cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2021, Issue

2021

8428 pages

ISSN:1687-5265

EISSN:1687-5273

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Copyright © 2021 Gongwen Xu et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 January 2021

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Cited By

Li SZhao YGuo LRen MLi JZhang LLi K(2024)Quantification and prediction of engagementInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10353661:1Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.ipm.2023.103536
Cheng LShi YLi LYu HWang XYan Z(2023)KLECA: knowledge-level-evolution and category-aware personalized knowledge recommendationKnowledge and Information Systems10.1007/s10115-022-01789-z65:3(1045-1065)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10115-022-01789-z
Huang SLiu QChen JHu XLiu ZLuo W(2022)A Design of a Simple Yet Effective Exercise Recommendation System in K-12 Online LearningArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium10.1007/978-3-031-11647-6_36(208-212)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1007/978-3-031-11647-6_36

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