系统梳理对话推荐系统仿真方法，揭示其在数据生成与评估中的关键作用。
A Literature Review on Simulation in Conversational Recommender Systems
- 构建四类研究分类框架，涵盖数据、算法、评估与实证研究。
- 大模型仿真可生成对话数据、优化算法并支持系统评估。
- 适合关注对话推荐系统研究方向的学者与开发者参考。
对话推荐系统（CRSs）通过多轮对话提供个性化推荐，受到广泛关注。本文构建了一个分类框架，将相关研究系统划分为数据集构建、算法设计、系统评估和实证研究四类，全面分析了CRSs研究中仿真方法的应用。分析表明，仿真方法在应对CRSs核心挑战中发挥关键作用。例如，基于大语言模型（LLM）的仿真方法被用于生成对话推荐数据、增强算法性能以及评估系统表现。尽管存在数据偏差、生成灵活性不足及文本语义与行为语义之间的鸿沟等挑战，这些挑战源于人机交互的复杂性，仿真方法仍具有显著潜力推动该领域发展。本综述全面总结了当前研究现状，并指出了未来有前景的研究方向。
原文摘要 · Abstract (English)
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.
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