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Conversational recommender systems (CRS) generate recommendations through aninteractive process. However, not all CRS approaches use human conversations astheir source of interaction data; the majority of prior CRS work simulatesinteractions by exchanging entity-level information. As a result, claims ofprior CRS work do not generalise to real-world settings where conversationstake unexpected turns, or where conversational and intent understanding is notperfect. To tackle this challenge, the research community has started toexamine holistic CRS, which are trained using conversational data collectedfrom real-world scenarios. Despite their emergence, such holistic approachesare under-explored.  We present a comprehensive survey of holistic CRS methods by summarizing theliterature in a structured manner. Our survey recognises holistic CRSapproaches as having three components: 1) a backbone language model, theoptional use of 2) external knowledge, and/or 3) external guidance. We alsogive a detailed analysis of CRS datasets and evaluation methods in realapplication scenarios. We offer our insight as to the current challenges ofholistic CRS and possible future trends.

A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems