Providing recommendations for communities of learners in MOOCs ecosystems
作者：
Highlights：
• A content-based recommendation using topic modeling and labeling techniques.
• Recommendation of part of courses from multiple providers to support learners.
• Implementation of a system with real data from providers, enabling the expansion.
• The model’s number of topics and document labels are automatically defined.
• Three quali-quantitative experiments confirm the system’s effectiveness.
摘要
•A content-based recommendation using topic modeling and labeling techniques.•Recommendation of part of courses from multiple providers to support learners.•Implementation of a system with real data from providers, enabling the expansion.•The model’s number of topics and document labels are automatically defined.•Three quali-quantitative experiments confirm the system’s effectiveness.
论文关键词：Online education systems,Content-based recommendation,Topic modeling,Non-negative matrix factorization,Unsupervised machine learning
论文评审过程：Received 19 August 2021, Revised 2 May 2022, Accepted 3 May 2022, Available online 20 May 2022, Version of Record 2 June 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117510
