Conversational Recommendation System (CRS) is a rapidly growing research area
that has gained significant attention alongside advancements in language
modelling techniques. However, the current state of conversational
recommendation faces numerous challenges due to its relative novelty and
limited existing contributions. In this study, we delve into benchmark datasets
for developing CRS models and address potential biases arising from the
feedback loop inherent in multi-turn interactions, including selection bias and
multiple popularity bias variants. Drawing inspiration from the success of
generative data via using language models and data augmentation techniques, we
present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model
performance while mitigating biases. Through extensive experiments on ReDial
and TG-ReDial benchmark datasets, we show a consistent improvement of CRS
techniques with our data augmentation approaches and offer additional insights
on addressing multiple newly formulated biases.

对话式推荐系统是一个快速发展的研究领域，随着语言建模技术的进步而备受关注。然而，目前的对话式推荐系统面临许多挑战，由于其相对新颖和现有贡献的有限性。本研究探讨了用于开发对话式推荐系统模型的基准数据集，并解决了多轮互动中固有的反馈循环所产生的选择偏差和多元受欢迎度偏差等潜在偏差。借鉴语言模型和数据增强技术的成功，我们提出了两种新的策略 “Once-Aug” 和 “PopNudge”，以增强模型性能并减轻偏差。通过对 ReDial 和 TG-ReDial 基准数据集进行广泛实验，我们展示了我们的数据增强方法在对话式推荐系统技术上的持续改进，并提供了解决多个新形成的偏差的更多见解。