根据用户职业定制对话策略，让销售型聊天机器人更高效。
From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents
- 基于用户职业设计轻量级对话策略，动态调整优先级。
- 职业差异对对话意图影响最大，显著改变交互效果。
- 适合需要个性化互动的销售类对话系统开发者。
随着代理型对话模型的快速发展，真实用户模拟研究对于优化对话策略至关重要。本文研究了一种面向销售场景的智能体，其对话策略依据用户的年龄、性别和职业等特征进行自适应调整。虽然年龄与性别会影响整体表现，但职业带来的对话意图差异最为显著。基于此发现，我们提出一种轻量级的职业条件化策略，引导智能体优先选择符合用户偏好的对话意图，从而实现更短且更成功的对话。研究结果强调了丰富模拟器用户画像的重要性，并证明简单的人物设定驱动策略可有效提升销售导向对话系统的性能。
原文摘要 · Abstract (English)
Amid the rapid rise of agentic dialogue models, realistic user-simulator studies are essential for tuning effective conversation strategies. This work investigates a sales-oriented agent that adapts its dialogue based on user profiles spanning age, gender, and occupation. While age and gender influence overall performance, occupation produces the most pronounced differences in conversational intent. Leveraging this insight, we introduce a lightweight, occupation-conditioned strategy that guides the agent to prioritize intents aligned with user preferences, resulting in shorter and more successful dialogues. Our findings highlight the importance of rich simulator profiles and demonstrate how simple persona-informed strategies can enhance the effectiveness of sales-oriented dialogue systems.
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