Course Selection Recommendation Based on Hybrid Recommendation Algorithms
- DOI
- 10.2991/978-94-6463-044-2_60How to use a DOI?
- Keywords
- Collaborative filtering algorithms; Content-based recommendation algorithms; Hybrid recommendation algorithms; Elective courses
- Abstract
In the era of national ‘double first-class’ and ‘high-level’ university construction, the course selection has become an important part of the curriculum system of many colleges and universities in China. Due to the shortcomings of the traditional course selection method, the need for a machine learning-based course selection recommendation system has become more and more urgent. The focus of this paper is to apply a hybrid recommendation algorithm to a course selection system for university students to achieve intelligent recommendations through a correlation between students and students and between students and courses. The experimental results show that the hybrid recommendation algorithm is very good at recommending courses for students in the university course selection recommendation system, and the recommended courses have good accuracy and reasonableness.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Luxiao Zhu AU - Ben Wang PY - 2022 DA - 2022/12/27 TI - Course Selection Recommendation Based on Hybrid Recommendation Algorithms BT - Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022) PB - Atlantis Press SP - 476 EP - 482 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-044-2_60 DO - 10.2991/978-94-6463-044-2_60 ID - Zhu2022 ER -