Go to ACL ARR 2025 May homepageExploring the Impact of Personality Traits on LLM-based Conversational Recommender Systems
Abstract: 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. Our experiments show that LLMs respond differently to users with varying personality traits. State-of-the-art LLMs can generate user responses that align well with specified traits, enabling CRSs to dynamically adopt persuasion strategies. Our analysis offers both quantitative and qualitative insights into the impact of personality traits on CRS performance.
Paper Type: Long
Research Area: Dialogue and Interactive Systems
Research Area Keywords: Conversational Recommender Systems, Personality Trait
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 7509
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