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

Xinwei Ren, Wei Yang, Xianliang Jiang, Guang Jin and Yan Yu
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Xinwei Ren: College of Science & Technology, Ningbo University, Ningbo 315300, 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

Sustainability, 2022, vol. 14, issue 5, 1-14

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.

Keywords: multimodal; deep learning; course recommendation; multimodal recommendation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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