Survey of Online Course Recommendation System

doi:10.3778/j.issn.1002-8331.2305-0162

Abstract

Abstract: The rapid development of online education has led to an explosive growth in the number of online courses, and learners are easily caught in inefficient access to course information caused by “course overload”, which has driven the emergence and development of online course recommendation systems. At present, online course recommendation systems have become a hot spot for research, and a large number of methods have been proposed in this area, so it is necessary to systematically review and analyze the latest research progress. This paper first summarizes the basic framework and related concepts of online course recommendation systems, and then focuses on comparing and analyzing various core recommendation methods used in existing online course recommendation systems, including these methods based on association rule mining, matrix factorization, probabilistic model, deep learning, intelligent optimization, semantic computing, and so on. Finally, this paper introduces various evaluation metrics of online course recommendation systems and publicly available datasets, and proposes the future development direction.

Key words: online course recommendation system, association rule mining, matrix factorization, probabilistic model, deep learning, intelligent optimization, semantic computing

摘要： 在线教育的快速发展使得在线课程数量爆炸式增长，学习者很容易陷入“课程过载”带来的课程信息获取效率低下的问题，这推动了在线课程推荐系统的产生和发展。目前在线课程推荐系统已成为研究热点，并且在该领域中提出了大量方法，有必要对最新的研究进展进行系统的梳理分析。首先归纳总结在线课程推荐系统的基本框架和相关概念。然后重点对比分析现有在线课程推荐系统采用的各类核心推荐方法，其中包括基于关联规则挖掘、基于矩阵分解、基于概率模型、基于深度学习、基于智能优化、基于语义计算等类型的方法。最后介绍在线课程系统的各种评价指标和公开可用的数据集，并展望未来的发展方向。

YU Peng, LIU Xingyu, CHENG Hao, YANG Jiaqi, CHEN Guohua, HE Chaobo. Survey of Online Course Recommendation System[J]. Computer Engineering and Applications, 2023, 59(22): 1-14.

余鹏, 刘星雨, 程颢, 杨佳琦, 陈国华, 贺超波. 在线课程推荐系统综述[J]. 计算机工程与应用, 2023, 59(22): 1-14.

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