Skip to Main content Skip to Navigation
Conference papers

Deep Reinforcement Learning for Personalized Recommendation of Distance Learning

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.
Document type :
Conference papers
Complete list of metadata
Contributor : Aurélien Vicentini Connect in order to contact the contributor
Submitted on : Wednesday, February 16, 2022 - 10:30:20 AM
Last modification on : Friday, May 20, 2022 - 11:02:48 AM




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⟩



Record views