Enhancing User Control: A Reinforcement Learning Framework for Breaking Filter Bubbles in Recommender Systems
Controllable recommender systems; Filter bubbles; Recommendation diversity; Recommendation systems; Reinforcement learning
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.
Deokar, Ruchira; Nanjundan, Preethi; George, Jossy P.; Behera, Naliniprava
Communications in Computer and Information Science;Volume;2243 CCIS;pp.154-167
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-031-81369-6_13 https://www.scopus.com/pages/publications/85219204438?origin=resultslist
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