Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
This work addresses the problem of personalizing conversational recommender systems for users by incorporating personality traits, though it is incremental as it builds on existing LLM and simulation methods.
The study tackled the challenge of understanding how personality traits affect conversational recommender systems by introducing PerCRS, an LLM-based simulation that generates diverse user responses aligned with personality traits, leading to dynamic adjustment of recommendation strategies.
Conversational Recommender Systems (CRSs) engage users in multi-turn interactions to deliver personalized recommendations. The emergence of large language models (LLMs) further enhances these systems by enabling more natural and dynamic user interactions. However, a key challenge remains in understanding how personality traits shape conversational recommendation outcomes. Psychological evidence highlights the influence of personality traits on user interaction behaviors. To address this, we introduce an LLM-based personality-aware user simulation for CRSs (PerCRS). The user agent induces customizable personality traits and preferences, while the system agent possesses the persuasion capability to simulate realistic interaction in CRSs. We incorporate multi-aspect evaluation to ensure robustness and conduct extensive analysis from both user and system perspectives. Experimental results demonstrate that state-of-the-art LLMs can effectively generate diverse user responses aligned with specified personality traits, thereby prompting CRSs to dynamically adjust their recommendation strategies. Our experimental analysis offers empirical insights into the impact of personality traits on the outcomes of conversational recommender systems.