Deep Reinforcement Learning for Personalized Recommendation of Distance Learning

Maroi Agrebi (1) , Mondher Sendi , Mourad Abed (1)

1 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

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.

Mots clés: Distance learning; Deep Reinforcement Learning; Personalization; Recommendation; Learner Profile

Domaines: Informatique [cs]

Maroi Agrebi, Mondher Sendi, Mourad Abed. Deep Reinforcement Learning for Personalized Recommendation of Distance Learning. World Conference on Information Systems and Technologies (WorldCist'19), Apr 2019, Illa da Toxa, Spain. pp.597-606, ⟨10.1007/978-3-030-16184-2_57⟩. ⟨hal-03576573⟩