让聊天机器人更懂用户，主动推理画像并策略性推荐。
Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action
- 通过对话动态推断用户画像，实现个性化交互。
- 在多类用户上提升推荐成功率与说服力。
- 基于真实行为数据构建模拟用户评估系统。
对话式推荐系统（CRSs）旨在通过对话为用户提供定制化推荐。传统CRS侧重于获取偏好和检索商品，但实际电商场景涉及更复杂的决策过程，用户需权衡多种因素。为此，我们提出对话式销售任务（CSALES），将偏好获取、推荐与说服整合至统一对话框架中。为支持真实且系统的评估，我们构建了基于大模型的用户模拟器CSUSER，其基于真实行为数据建模细粒度用户画像。同时提出CSI对话销售代理，可主动推断上下文用户画像，并通过对话策略性选择行动。大量实验表明，CSI在不同用户画像下显著提升了推荐成功率与说服效果。
原文摘要 · Abstract (English)
Conversational Recommender Systems (CRSs)aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving items, real-world e-commerce interactions involve more complex decision-making, where users consider multiple factors beyond simple attributes. To capture this complexity, we introduce Conversational Sales (CSALES), a novel task that integrates preference elicitation, recommendation, and persuasion within a unified conversational framework. To support realistic and systematic evaluation, we present CSUSER, an evaluation protocol with LLM-based user simulator grounded in real-world behavioral data by modeling fine-grained user profiles for personalized interaction. We also propose CSI, a conversational sales agent that proactively infers contextual user profiles and strategically selects actions through conversation. Comprehensive experiments show that CSI significantly improves both recommendation success and persuasive effectiveness across diverse user profiles.
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