A recurrent neural network-based recommender system framework and prototype for sequential E-learning
- Publisher:
- WORLD SCIENTIFIC
- Publication Type:
- Conference Proceeding
- Citation:
- Developments of Artificial Intelligence Technologies in Computation and Robotics, 2020, pp. 488-495
- Issue Date:
- 2020-10
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In the fast pace of life, E-learning has become a new way for self-improvement and competitiveness. The recommendation is needed in an E-learning system to filter suitable courses for users when they are facing a massive amount of information in course enrolment. However, due to the complexity of each learning course and the change of user interest, it is challenging to provide accurate recommendations. This paper proposes an E-learning recommender system that combines the recurrent neural network (RNN) and content-based technique to support users in course selection. The content-based techniques are to mine the relationships between courses, and the recurrent neural network is to extract user interests with a series of his/her enrolled courses. The proposed E-learning recommender system framework takes sequential connections into consideration. It intends to provide students with more precise course recommendations. The system is implemented with the Django framework and ElephantSQL cloud database and deployed on the Amazon Elastic Compute Cloud.
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