Exploring the Impact of Large Language Model-Simulated Personalities on Recommendation

In recent years, Large Language Model-based Recommendation Systems (LLMRec) have achieved remarkable advancements. By designing effective prompts, LLMs can understand user interests and either directly generate recommendations based on users' interests or assist traditional recommendation systems in enhancing recommendation performance. However, existing LLMRec predominantly leverage LLMs as static tools for user interest understanding, ignoring the dynamic behavioral patterns of LLMs when simulating human personalities. While some studies explore LLMs' human-like behaviors in conversational scenarios, their impacts on recommendation systems remain underexplored. To fill these gaps, this paper aims to investigate how LLM-simulated personalities interact with users of varying personalities in recommendation systems, particularly focusing on whether LLM-simulated personalities exhibit behaviors align with Social Homophily Theory which is well-documented in human social interactions but unexplored in LLMRec. We conducted experiments on two public datasets, inducing LLMs to simulate different human personalities based on the Big Five Personality Traits through prompt engineering, and incorporating the generated user profiles into the Knowledge Augmented Recommendation framework to evaluate performance across various recommendation models. Experimental results demonstrate that inducing LLMs to simulate personality in the KAR framework significantly influences recommendation performance. Moreover, LLM-simulated personalities exhibit a preference for users with similar personalities, mirroring the Social Homophily Theory observed in human behavior.