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A review of deep learning-based recommender system in e-learning environments

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Abstract

While the recent emergence of a large number of online course resources has made life more convenient for many people, it has also caused information overload. According to a user’s situation and behavior, course recommendation systems can recommend courses of interest to the user, so that the user can quickly sift through a massive amount of information to find courses that meet his or her needs. This paper provide a systematic review of deep learning-based recommendation systems in e-learning environments. Firstly, the concept of recommendation systems is introduced in e-learning environments, and present a comprehensive survey and classification of deep learning techniques for course recommendation. And then, a detailed analysis of existing recommendation system is conducted based on the collected literature, and an overall course recommendation system framework is presented. Subsequently, this artical main focus is on multilayer perceptual machines, recurrent neural networks, convolutional neural networks, neural attention mechanisms, and deep reinforcement learning-based recommendation, and summarize the existing research on the use of the five techniques mentioned above in e-learning environments. The last section discusses seven flaws in the current recommendation systems used in e-learning environments and identify opportunities for future research.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Nos. U1811264, U1711263, 61966009), the Natural Science Foundation of Guangxi Province (Nos. 2018GXNSFDA281045, 2018GXNSFAA138090,2019GXNSFBA245049, 2019GXNSFBA245059), Guangxi Key Laboratory of Trusted Software (No. KX202058) and the Innovation Project of Guang Xi Graduate Education (No.YCBZ2021072).

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  1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Tieyuan Liu, Qiong Wu, Liang Chang & Tianlong Gu

  2. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

    Qiong Wu & Liang Chang

  3. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China

    Tieyuan Liu

  4. College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, 510632, China

    Tianlong Gu

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  1. Tieyuan Liu
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Liu, T., Wu, Q., Chang, L. et al. A review of deep learning-based recommender system in e-learning environments. Artif Intell Rev 55, 5953–5980 (2022). https://doi.org/10.1007/s10462-022-10135-2

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