The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles. However, concerns regarding biases in these systems have emerged. While individual components of CRS have been subject to bias studies, a literature gap remains in understanding specific biases unique to CRS and how these biases may be amplified or reduced when integrated into complex CRS models. In this paper, we provide a concise review of biases in CRS by surveying recent literature. We examine the presence of biases throughout the system's pipeline and consider the challenges that arise from combining multiple models. Our study investigates biases in classic recommender systems and their relevance to CRS. Moreover, we address specific biases in CRS, considering variations with and without natural language understanding capabilities, along with biases related to dialogue systems and language models. Through our findings, we highlight the necessity of adopting a holistic perspective when dealing with biases in complex CRS models.

翻译：语言模型日益普及，引发了工业和学术界对对话推荐系统（CRS）的兴趣。然而，关于这些系统中偏差的担忧也随之浮现。尽管CRS的各个组成部分已受到偏差研究关注，但文献中仍存在空白，未能充分理解CRS特有的偏差类型，以及这些偏差在整合到复杂CRS模型时可能被放大或削弱的方式。本文通过梳理近期文献，对CRS中的偏差进行了简明综述。我们考察了系统全流程中偏差的存在情况，并思考了多模型组合所带来的挑战。本研究探讨了经典推荐系统中的偏差及其与CRS的相关性。此外，我们分析了CRS特有的偏差，考虑了具备与不具备自然语言理解能力的变体，以及对话系统和语言模型相关的偏差。基于研究发现，我们强调了在处理复杂CRS模型中的偏差时采用全局视角的必要性。