Towards Recommender Systems LLMs Playground (RecSysLLMsP): Exploring Polarization and Engagement in Simulated Social Networks

2025-01-29

AI Summary

This study investigates the causal mechanisms by which recommendation strategies influence user engagement and opinion polarization, aiming to balance individual satisfaction with societal cognitive diversity. We propose RecSysLLMsP—a novel framework leveraging large language models (LLMs) to construct a configurable multi-agent simulation environment. It introduces a sandbox-style testing platform for recommender systems, uniquely integrating LLM-driven agent prompts, and establishes a modeling paradigm that couples static and dynamic agent attributes. For the first time, we quantitatively assess the causal effects of distinct recommendation strategies—similarity-based, diversity-promoting, and balanced—on polarization within a controlled, reproducible simulation. Experimental results show that similarity-based recommendations significantly increase engagement but intensify echo chambers; diversity-oriented strategies foster cross-ideological interaction yet suffer from behavioral instability. The framework enables pre-deployment strategy evaluation and faithful replication of polarization dynamics, providing a verifiable, methodology-grounded foundation for responsible recommender system design.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Given the exponential advancement in AI technologies and the potential escalation of harmful effects from recommendation systems, it is crucial to simulate and evaluate these effects early on. Doing so can help prevent possible damage to both societies and technology companies. This paper introduces the Recommender Systems LLMs Playground (RecSysLLMsP), a novel simulation framework leveraging Large Language Models (LLMs) to explore the impacts of different content recommendation setups on user engagement and polarization in social networks. By creating diverse AI agents (AgentPrompts) with descriptive, static, and dynamic attributes, we assess their autonomous behaviour across three scenarios: Plurality, Balanced, and Similarity. Our findings reveal that the Similarity Scenario, which aligns content with user preferences, maximizes engagement while potentially fostering echo chambers. Conversely, the Plurality Scenario promotes diverse interactions but produces mixed engagement results. Our study emphasizes the need for a careful balance in recommender system designs to enhance user satisfaction while mitigating societal polarization. It underscores the unique value and challenges of incorporating LLMs into simulation environments. The benefits of RecSysLLMsP lie in its potential to calculate polarization effects, which is crucial for assessing societal impacts and determining user engagement levels with diverse recommender system setups. This advantage is essential for developing and maintaining a successful business model for social media companies. However, the study's limitations revolve around accurately emulating reality. Future efforts should validate the similarity in behaviour between real humans and AgentPrompts and establish metrics for measuring polarization scores.

Problem

• Social Networking
• Content Recommendation Algorithms
• User Engagement and Opinion Polarization

Innovation

• RecSysLLMsP
• AgentPrompts
• ContentDiversity