Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Deokar, Ruchira; Nanjundan, Preethi; George, Jossy P.; Behera, Naliniprava Title A name given to the resource Enhancing User Control: A Reinforcement Learning Framework for Breaking Filter Bubbles in Recommender Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2243 CCIS;pp.154-167 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-81369-6_13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-81369-6_13</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85219204438?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85219204438?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Deokar R., CHRIST University, Bengaluru, India; Nanjundan P., CHRIST University, Bengaluru, India; George J.P., CHRIST University, Bengaluru, India; Behera N., KIIT Deemed to be University, Odisha, Bhubaneswar, India Description An account of the resource In an age of information overload, recommendation systems play an important role in providing personalized content to users. However, traditional recommendation systems often create filter bubbles, limiting the types of content users are exposed to. Based on the research presented in the article Breaking the Filter Bubble: A Reinforcement Learning Framework for Controllable Recommender Systems, this article proposes a new approach to further improve the controllability and diversity of recommendations. By using reinforcement learning techniques, the proposed framework aims to break the filter bubble by providing users with more diverse content recommendations while maintaining high recommendation accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of this approach in suppressing recommendation concentration and improving recommendation diversity. The results of this study contribute to the further development of controllable recommendation systems and provide insights into solving the filter bubble problem in recommendation systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Controllable recommender systems; Filter bubbles; Recommendation diversity; Recommendation systems; Reinforcement learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303181368-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online