Balancing Benefits and Risks: RL Approaches for Addiction-Aware Social Media Recommenders

Social media platforms provide valuable opportunities for users to gather information, interact with friends, and enjoy entertainment. However, their addictive potential poses significant challenges, including overuse and negative psycho-logical or behavioral impacts [4, 2, 8]. This study explores strategies to mitigate compulsive social media usage while preserving its benefits and ensuring economic sustainability, focusing on recommenders that promote balanced usage. We analyze user behaviors arising from intrinsic diversities and environmental interactions, offering insights for next-generation social media recommenders that prioritize well-being. Specifically, we examine the temporal predictability of overuse and addiction using measures available to recommenders, aiming to inform mechanisms that prevent addiction while avoiding user disengagement [7]. Building on RL-based computational frameworks for addiction modelling [6], our study introduces: - A recommender system adapting to user preferences, introducing non-stationary and non-Markovian dynamics. - Differentiated state representations for users and recommenders to capture nuanced interactions. - Distinct usage conditions-light and heavy use-addressing RL's limitations in distinguishing prolonged from healthy engagement. - Complexity in overuse impacts, highlighting their role in user adaptation [7]. Simulations demonstrate how model-based (MB) and model-free (MF) decision-making interact with environmental dynamics to influence user behavior and addiction. Results reveal the significant role of recommender systems in shaping addiction tendencies or fostering healthier engagement. These findings support ethical, adaptive recommender design, advancing sustainable social media ecosystems [9, 1]. Keywords: multi-agent systems, recommender systems, addiction, social media