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

https://hal-uphf.archives-ouvertes.fr/hal-03576573
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

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

9