mlsanityData to 7 Oct 2026

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

arXiv 2309.07682 ↗First posted 14 Sep 2023cs.CL cs.IRcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 55 of its 82 references that are in the map. First posted on arXiv 14 Sep 2023.

Builds on

55 of 82 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factorscited by 9
  2. 2024Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systemscited by 8
  3. 2026Interplay: Training Independent Simulators for Reference-Free Conversational Recommendationcited by 0