提出可模拟用户犹豫行为的推荐系统评估框架，更真实反映决策过程。
Decision-aware User Simulation Agent for Evaluating Conversational Recommender Systems
- 基于心理经济学设计决策模块，区分选品与决策两个阶段。
- 在多场景下显著降低虚假高接受率，缓解选择过载下的不实行为。
- 适合研究推荐系统评估、人机交互中的真实决策建模者使用。
对话式推荐系统（CRS）日益依赖用户模拟器进行自动化评估。现有模拟框架大多未显式建模人类决策过程，基于大模型的模拟器常表现出远超真实的强信息处理能力，很少出现真实消费者常见的犹豫或推迟决策现象，导致接受概率过高。为此，我们提出Hesitator——一种基于理论的用户模拟框架，显式建模在选择过载下的决策行为。该框架引入模块化决策模块，将基于效用的物品选择与受过载影响的承诺决策相分离。在多种用户模拟框架、领域、销售模式及大模型基座上的实验表明，集成该模块能持续缓解过载条件下的非现实行为。此外，Hesitator成功复现了心理学经济学中的经典行为模式，验证了其对人类决策行为的建模能力。
原文摘要 · Abstract (English)
Conversational recommender systems (CRS) increasingly rely on user simulators for automated evaluation of sales agents. A key requirement for such simulators is the ability to model human decision-making. However, most existing simulation frameworks do not explicitly model the internal decision process, and LLM-based simulators often exhibit unrealistically strong information-processing capabilities, rarely exhibit the hesitation or decision deferral commonly observed in real consumer behavior, resulting in overly high acceptance probabilities. To address this limitation, we propose Hesitator, a theory-grounded user simulation framework that explicitly models human decision-making under choice overload. The framework introduces a modular Decision Module that separates utility-based item selection from overload-aware commitment decisions. Experiments across multiple user simulation frameworks, domains, sales modes, and LLM backbones show that integrating our module consistently mitigates unrealistic behaviors under increasing overload conditions. Furthermore, Hesitator reproduces established behavioral patterns from psychological economics, demonstrating its ability to model human decision behavior.
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