Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system's ability to provide personalized recommendations and improving the overall user experience. CRS has demonstrated significant promise, prompting researchers to concentrate their efforts on developing user simulators that are both more realistic and trustworthy. The emergence of Large Language Models (LLMs) has marked the onset of a new epoch in computational capabilities, exhibiting human-level intelligence in various tasks. Research efforts have been made to utilize LLMs for building user simulators to evaluate the performance of CRS. Although these efforts showcase innovation, they are accompanied by certain limitations. In this work, we introduce a Controllable, Scalable, and Human-Involved (CSHI) simulator framework that manages the behavior of user simulators across various stages via a plugin manager. CSHI customizes the simulation of user behavior and interactions to provide a more lifelike and convincing user interaction experience. Through experiments and case studies in two conversational recommendation scenarios, we show that our framework can adapt to a variety of conversational recommendation settings and effectively simulate users' personalized preferences. Consequently, our simulator is able to generate feedback that closely mirrors that of real users. This facilitates a reliable assessment of existing CRS studies and promotes the creation of high-quality conversational recommendation datasets.

翻译：对话推荐系统利用用户的实时反馈动态建模其偏好，从而增强系统提供个性化推荐的能力并提升整体用户体验。对话推荐系统展现出显著发展潜力，促使研究人员致力于开发更真实、更可信的用户模拟器。大语言模型的出现标志着计算能力新时代的开端，其在各类任务中展现出类人智能水平。已有研究尝试利用大语言模型构建用户模拟器以评估对话推荐系统性能，但这些创新性工作仍存在一定局限性。本文提出一种可控、可扩展且人机协同的模拟器框架，通过插件管理器在不同阶段调控用户模拟器的行为。该框架通过定制化模拟用户行为与交互过程，提供更逼真、更具说服力的用户交互体验。通过在两个对话推荐场景中的实验与案例研究，我们证明该框架能适应多样化的对话推荐设置，有效模拟用户的个性化偏好。由此，我们的模拟器能够生成高度接近真实用户的反馈，为现有对话推荐系统研究提供可靠评估，并促进高质量对话推荐数据集的构建。