Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information. As a result, claims of prior CRS work do not generalise to real-world settings where conversations take unexpected turns, or where conversational and intent understanding is not perfect. To tackle this challenge, the research community has started to examine holistic CRS, which are trained using conversational data collected from real-world scenarios. Despite their emergence, such holistic approaches are under-explored. We present a comprehensive survey of holistic CRS methods by summarizing the literature in a structured manner. Our survey recognises holistic CRS approaches as having three components: 1) a backbone language model, the optional use of 2) external knowledge, and/or 3) external guidance. We also give a detailed analysis of CRS datasets and evaluation methods in real application scenarios. We offer our insight as to the current challenges of holistic CRS and possible future trends.

翻译：对话式推荐系统（CRS）通过交互过程生成推荐结果。然而，并非所有CRS方法都将人类对话作为交互数据来源；大多数先前CRS研究通过交换实体级信息来模拟交互。因此，先前CRS研究的结论无法推广至现实场景——在这些场景中，对话可能发生意外转折，或对话意图理解并非完美。为应对这一挑战，研究界开始探索全局对话式推荐系统（holistic CRS），这类系统利用从真实场景中收集的对话数据进行训练。尽管此类全局方法已崭露头角，但其研究仍不充分。我们通过结构化文献综述，对全局CRS方法进行了全面调研。本综述将全局CRS方法归纳为三个组成部分：1）骨干语言模型，2）可选的外部知识，和/或3）可选的外部引导。我们还详细分析了真实应用场景中的CRS数据集与评估方法。最后，我们就全局CRS当前面临的挑战及未来可能的发展趋势提出了见解。