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      Enhancing Content Recommendation in Real-Time: A Live Recommender System Leveraging Deep Reinforcement Learning

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            Abstract

            In light of the rapid emergence of deep reinforcement learning (DRL) in the field of recommender systems, this paper aims to provide a comprehensive overview of recent trends in this area. We begin by highlighting the motivation behind applying DRL in recommender systems, followed by a samples of current DRL-based approaches and existing methods. We also discuss emerging topics, open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for researchers from academia and industry interested in DRL-based recommender systems, while identifying noteworthy opportunities for further research in this exciting field.

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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            17 April 2023
            Affiliations
            [1 ] ;
            Author notes
            Author information
            https://orcid.org/0009-0009-0252-2746
            Article
            10.14293/PR2199.000069.v1
            aa2406f8-ebe7-40e1-8f9f-ae68866780a9

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 17 April 2023
            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Information systems & theory
            Recommender systems, Deep reinforcement learning (DLR), Dynamic environments, Pessimistic reward modeling, Sim2real transfer learning

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