用大模型模拟不同性格用户，研究性格如何影响推荐效果。
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
- 构建性格可调的虚拟用户，与推荐系统对话
- 大模型能生成符合性格特征的多样回应
- 适合研究个性化推荐与心理因素的学者
对话式推荐系统（CRS）通过多轮交互实现个性化推荐。大语言模型（LLMs）的兴起使交互更自然动态，但人格特质如何影响推荐结果仍不明确。心理学研究表明人格影响用户行为。为此，我们提出基于大模型的性格感知用户仿真框架（PerCRS），其中用户代理可自定义人格特质与偏好，系统代理具备说服能力，模拟真实交互。采用多维度评估确保稳健性，并从用户与系统双视角展开分析。实验表明，先进大模型能有效生成与指定人格一致的多样化用户响应，促使系统动态调整推荐策略。实证分析揭示了人格特质对对话推荐系统结果的影响。
原文摘要 · Abstract (English)
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.
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