A Literature Review on Simulation in Conversational Recommender Systems

• URL: http://arxiv.org/abs/2506.20291v1
• Date: Wed, 25 Jun 2025 09:53:35 GMT
• Title: A Literature Review on Simulation in Conversational Recommender Systems
• Authors: Haoran Zhang, Xin Zhao, Jinze Chen, Junpeng Guo,
• Abstract summary: Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn dialogues.<n>This review developed a taxonomy framework to categorize relevant publications into four groups: dataset construction, algorithm design, system evaluation, and empirical studies.<n>Our analysis reveals that simulation methods play a key role in tackling CRSs' main challenges.
• Score: 19.308825521235605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational Recommender Systems (CRSs) have garnered attention as a novel approach to delivering personalized recommendations through multi-turn dialogues. This review developed a taxonomy framework to systematically categorize relevant publications into four groups: dataset construction, algorithm design, system evaluation, and empirical studies, providing a comprehensive analysis of simulation methods in CRSs research. Our analysis reveals that simulation methods play a key role in tackling CRSs' main challenges. For example, LLM-based simulation methods have been used to create conversational recommendation data, enhance CRSs algorithms, and evaluate CRSs. Despite several challenges, such as dataset bias, the limited output flexibility of LLM-based simulations, and the gap between text semantic space and behavioral semantics, persist due to the complexity in Human-Computer Interaction (HCI) of CRSs, simulation methods hold significant potential for advancing CRS research. This review offers a thorough summary of the current research landscape in this domain and identifies promising directions for future inquiry.

Related papers

• Display Content, Display Methods and Evaluation Methods of the HCI in Explainable Recommender Systems: A Survey [15.083183325446]
  Explainable Recommender Systems (XRS) aim to provide users with understandable reasons for the recommendations generated by these systems.<n>Recent research has increasingly focused on the algorithms, display, and evaluation methodologies of XRS.<n>In this study, we synthesize existing literature and surveys on XRS, presenting a unified framework for its research and development.
  arXiv  Detail & Related papers  (2025-05-14T01:48:59Z)
• Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey [64.08485471150486]
  This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings.<n>We systematically reviewed nearly 250 scholarly sources, capturing the state of the art from various venues of publication.
  arXiv  Detail & Related papers  (2025-03-28T14:08:40Z)
• Graph Retrieval-Augmented LLM for Conversational Recommendation Systems [52.35491420330534]
  G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems) is a training-free framework that combines graph retrieval-augmented generation and in-context learning.<n>G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.
  arXiv  Detail & Related papers  (2025-03-09T03:56:22Z)
• Large Language Models as Evaluators for Conversational Recommender Systems: Benchmarking System Performance from a User-Centric Perspective [38.940283784200005]
  This study proposes an automated LLM-based CRS evaluation framework.<n>It builds upon existing research in human-computer interaction and psychology.<n>We use this framework to evaluate four different conversational recommender systems.
  arXiv  Detail & Related papers  (2025-01-16T12:06:56Z)
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems [54.78815548652424]
  Conversational recommender systems generate recommendations through an interactive process. Not all CRS approaches use human conversations as their source of interaction data. holistic CRS are trained using conversational data collected from real-world scenarios.
  arXiv  Detail & Related papers  (2023-09-14T12:55:23Z)
• Improving Conversational Recommendation Systems via Counterfactual Data Simulation [73.4526400381668]
  Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. We propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs.
  arXiv  Detail & Related papers  (2023-06-05T12:48:56Z)
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models [115.7508325840751]
  The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs) In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
  arXiv  Detail & Related papers  (2023-05-22T15:12:43Z)
• Synthetic Data-Based Simulators for Recommender Systems: A Survey [55.60116686945561]
  This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation. We start with the motivation behind the development of frameworks implementing the simulations -- simulators. We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
  arXiv  Detail & Related papers  (2022-06-22T19:33:21Z)
• T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems [5.592114738742928]
  We introduce T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems. To demonstrate the flexibility of T-RECS, we perform a replication of two prior simulation-based research on sociotechnical systems.
  arXiv  Detail & Related papers  (2021-07-19T15:16:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.