Abstract
Nowadays, distance learning becomes more diverse and popular. Increasingly universities are currently working to offer their online courses (MOOC, SPOC, SMOC, SSOC, etc.) in the form of courses providing learners with a wide variety of choices. However, this multi-criteria choice is complex. In this paper, we propose a personalized recommendation system based on Deep Reinforcement Learning that suggests for learners a most appropriate course according to specificities of each one such as their profile, needs and competences. To validate our system, the later has been tested over a set of real students. The obtained results of our study are in favor of the robustness of our system.
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Agrebi, M., Sendi, M., Abed, M. (2019). Deep Reinforcement Learning for Personalized Recommendation of Distance Learning. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_57
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DOI: https://doi.org/10.1007/978-3-030-16184-2_57
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