Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-recommender conversations. Traditional simulation approaches often utilize a single large language model (LLM) that generates entire conversations with prior knowledge of the target items, leading to scripted and artificial dialogues. We propose a reference-free simulation framework that trains two independent LLMs, one as the user and one as the conversational recommender. These models interact in real-time without access to predetermined target items, but preference summaries and target attributes, enabling the recommender to genuinely infer user preferences through dialogue. This approach produces more realistic and diverse conversations that closely mirror authentic human-AI interactions. Our reference-free simulators match or exceed existing methods in quality, while offering a scalable solution for generating high-quality conversational recommendation data without constraining conversations to pre-defined target items. We conduct both quantitative and human evaluations to confirm the effectiveness of our reference-free approach.

翻译：训练对话式推荐系统（CRS）需要大量的对话数据，而大规模收集这些数据颇具挑战性。为解决此问题，研究人员采用了模拟用户与推荐器对话的方法。传统模拟方法常利用单一大型语言模型（LLM）生成完整对话，该模型预先知晓目标物品信息，导致对话显得刻板且不自然。我们提出一种参考无关的模拟框架，训练两个独立的LLM，分别充当用户和对话推荐器。这两个模型在没有预定义目标物品的情况下实时交互，仅基于偏好总结和目标属性运作，使推荐器能够在对话过程中真实推断用户偏好。该方法生成的对话更加真实多样，能够紧密模拟真实的人机交互场景。我们的参考无关模拟器在质量上达到或超越现有方法，同时提供了一种可扩展的解决方案，用于生成高质量的对话式推荐数据，且不受限于预定义的目标物品。我们通过量化评估和人工评估验证了参考无关方法的有效性。