用大模型模拟用户性格，生成更真实的推荐系统行为数据。
PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
- 基于大模型和五大性格特质建模用户行为。
- 生成数据与真实亚马逊数据统计特征高度一致。
- 适合评估推荐系统在不同性格用户下的表现。
传统离线评估方法因行为信号稀疏、数据噪声和用户性格建模不足，难以捕捉现代平台的复杂性。现有仿真框架虽能生成合成数据，但难以还原行为多样性。为此，我们提出性格驱动的用户行为仿真器（PUB），一种基于大语言模型的仿真框架，融合五大性格特质以建模个性化行为。PUB从行为日志（如评分、评论）和物品元数据中动态推断用户性格，并生成保持真实数据统计特性的合成交互。在亚马逊评论数据集上的实验表明，PUB生成的日志与真实行为高度吻合，揭示了性格特质与推荐结果间的有意义关联。结果表明，该性格驱动仿真器可显著提升推荐系统评估的可扩展性、可控性和保真度，为资源密集型真实实验提供高效替代方案。
原文摘要 · Abstract (English)
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulation framework that integrates the Big Five personality traits to model personalised user behaviour. PUB dynamically infers user personality from behavioural logs (e.g., ratings, reviews) and item metadata, then generates synthetic interactions that preserve statistical fidelity to real-world data. Experiments on the Amazon review datasets show that logs generated by PUB closely align with real user behaviour and reveal meaningful associations between personality traits and recommendation outcomes. These results highlight the potential of the personality-driven simulator to advance recommender system evaluation, offering scalable, controllable, high-fidelity alternatives to resource-intensive real-world experiments.
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