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A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

Author

Listed:
  • Xinwei Ren

    (College of Science & Technology, Ningbo University, Ningbo 315300, China
    College of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo 315211, China)

  • Wei Yang

    (College of Science & Technology, Ningbo University, Ningbo 315300, China)

  • Xianliang Jiang

    (College of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo 315211, China)

  • Guang Jin

    (College of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo 315211, China)

  • Yan Yu

    (College of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo 315211, China)

Abstract

With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

Suggested Citation

  • Xinwei Ren & Wei Yang & Xianliang Jiang & Guang Jin & Yan Yu, 2022. "A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2907-:d:762468
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    Citations

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    Cited by:

    1. Yutong Fang & Jianzhi Deng & Fengming Zhang & Hongyan Wang, 2023. "An Intelligent Question-Answering Model over Educational Knowledge Graph for Sustainable Urban Living," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    2. Miao, Ruomu & Li, Benqian, 2022. "A user-portraits-based recommendation algorithm for traditional short video industry and security management of user privacy in social networks," Technological Forecasting and Social Change, Elsevier, vol. 185(C).

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