Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.

翻译：对话推荐系统（CRS）旨在通过自然语言对话提供推荐服务。尽管已有多种方法被提出用于开发高效的CRS，但它们通常依赖于充足的训练数据进行学习。由于面向推荐的对话数据集难以人工标注，现有CRS方法常因训练数据稀缺而面临训练不足的问题。为解决此问题，本文提出了一种面向CRS的反事实数据模拟方法（称为CFCRS），以缓解CRS中的数据稀缺性。该方法基于反事实数据增强框架构建，通过逐步将真实对话中的用户偏好改写融入其中，而不干扰整个对话流程。在开发过程中，我们通过对话中涉及的实体对用户偏好进行表征，并组织对话流程，进而设计了一种基于对话流程语言模型的多阶段推荐对话模拟器。在学习的用户偏好和对话模式的指导下，该流程语言模型能够生成合理且连贯的对话流程，并进一步实现为完整对话。基于该模拟器，我们对目标用户交互实体的表征进行干预，并设计了一种带课程调度的对抗训练方法，可逐步优化数据增强策略。大量实验表明，我们的方法能够持续提升多个竞品CRS的性能，并优于其他数据增强方法，尤其在训练数据有限时效果显著。我们的代码已开源至 https://github.com/RUCAIBox/CFCRS。