A personalized recommendation framework based on MOOC system integrating deep learning and big data. (March 2023)

• Record Type: Journal Article
• Authors: Li, Bifeng; Li, Gangfeng; Xu, Jingxiu; Li, Xueguang; Liu, Xiaoyan; Wang, Mei; Lv, Jianhui
• Is Part Of: Computers & electrical engineering. Volume 106(2023)
• Publication Date: 2023-03
• Subjects: Personalized recommendation -- MOOC system -- BERT -- Deep learning -- Big data; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108571
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: We propose a personalized course recommendation method based on BERT model, which integrates deep learning and big data technology based on MOOC system. We design a domain feature difference learning strategy, which is able to learn the difference features between course contents to better extract information in the text and improve the recommendation performance of the model. We experimentally demonstrate the effectiveness of the method proposed in this paper on the open MoocCube datasets. Abstract: Finding the courses that users are interested in quickly in the massive data can make a very important contribution to the accurate dissemination of knowledge. In this paper, we integrate the deep learning and big data technology to investigate a personalized recommendation method based on Massive Open Online Course (MOOC) system. Based on the Bidirectional Encoder Representations from Transformers (BERT) model, we propose some corresponding strategies to improve the accuracy of the recommendation system. First, we introduce the acquisition and preprocessing of the open dataset. Second, we design a recommendation model framework by taking advantage of the BERT model and incorporating a self-attention mechanism. Finally, to obtain deep feature information between course texts, we design a domain feature difference learning strategy to improve the model's recommendation performance. The results of our experiments prove that the proposed model in this paper performs good recommendation results compared with other methods. Graphical abstract: Image, graphical abstract