A personalized recommendation framework based on MOOC system integrating deep learning and big data

被引:10
|
作者
Li, Bifeng [1 ]
Li, Gangfeng [2 ]
Xu, Jingxiu [1 ]
Li, Xueguang [3 ]
Liu, Xiaoyan [4 ]
Wang, Mei [5 ]
Lv, Jianhui [6 ]
机构
[1] Huanggang Normal Univ, Sch Comp Sci & Technol, Huanggang 438000, Peoples R China
[2] Huanggang High Sch Hubei Prov, Huanggang 438000, Peoples R China
[3] Henan Inst Technol, Xinxiang 453000, Henan, Peoples R China
[4] Jiangxi Tellhow Animat Vocat Coll, Dept Virtual Real, Nanchang 330200, Peoples R China
[5] Shandong First Med Univ, Coll Med Informat Engn, Tai An 271016, Peoples R China
[6] Peng Cheng Lab, Shenzhen 518057, Peoples R China
基金
中国国家自然科学基金;
关键词
Personalized recommendation; MOOC system; BERT; Deep learning; Big data;
D O I
10.1016/j.compeleceng.2022.108571
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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 ex-periments prove that the proposed model in this paper performs good recommendation results compared with other methods.
引用
收藏
页数:11
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