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

Luca Bolis, Stefano Livella, Sabrina Patania, Dimitri Ognibene, Matteo Papini, Kenji Morita

TL;DR

This study examines the temporal predictability of overuse and addiction using measures available to recommenders, aiming to inform mechanisms that prevent addiction while avoiding user disengagement, and introduces a recommender system adapting to user preferences, introducing non-stationary and non-Markovian dynamics.

Abstract

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

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

TL;DR

This study examines the temporal predictability of overuse and addiction using measures available to recommenders, aiming to inform mechanisms that prevent addiction while avoiding user disengagement, and introduces a recommender system adapting to user preferences, introducing non-stationary and non-Markovian dynamics.

Abstract

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

Paper Structure

This paper contains 9 sections, 1 equation, 4 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. User modeling
  4. Recommender System modelling
  5. Environment design
  6. Results
  7. Key differences in model parameterization:
  8. Misrepresentation of the Environment:
  9. Conclusion and future directions

Figures (4)

  • Figure 1: Representation of states and actions for the simplified environment (left) and the advanced environment with refined recommender system (right).
  • Figure 2: Number of agents who identified the optimal policy in the environment with the refinement of the recommender system.
  • Figure 3: (Left) MBUS performance comparison in the misrepresented environment. (Right) Q-values of the recommender system in the refined environment.
  • Figure 4: Number of agents who identified the optimal policy in scenarios with misrepresentation of the environment, for $\beta$=0 and $\beta$=0.5.