Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
作者：
Highlights：
• Proposal of a multi-criteria hybrid recommendation system for university students.
• Deployment of a wide number of criteria including both student and course information.
• Deployment of different similarity measures for computing distance between criteria.
• Application of document analysis as criterion of the recommendation system.
• Design and development of a GA to assign weights to all the considered criteria.
• Design and development of a GA to choose the most appropriate similarity measures.
• Design and development of a GA to choose the most appropriate neighborhood size.
摘要
•Proposal of a multi-criteria hybrid recommendation system for university students.•Deployment of a wide number of criteria including both student and course information.•Deployment of different similarity measures for computing distance between criteria.•Application of document analysis as criterion of the recommendation system.•Design and development of a GA to assign weights to all the considered criteria.•Design and development of a GA to choose the most appropriate similarity measures.•Design and development of a GA to choose the most appropriate neighborhood size.
论文关键词：Recommendation system,Course recommendation,Hybrid multi-criteria filtering,Genetic algorithm
论文评审过程：Received 31 January 2019, Revised 10 December 2019, Accepted 11 December 2019, Available online 17 December 2019, Version of Record 18 May 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.105385
