面向个性化对话销售智能体：用于策略性行动的上下文用户画像
Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action
AI总结：
本文提出对话式销售任务CSALES及智能体CSI，通过推断上下文用户画像并策略性选择行动，在统一框架中整合偏好挖掘、推荐与说服，显著提升推荐成功率和说服效果。
摘要：
对话式推荐系统（CRSs）旨在通过对话与用户互动，以提供量身定制的推荐。虽然传统的CRSs侧重于挖掘用户偏好并检索物品，但现实世界的电子商务互动涉及更复杂的决策过程，用户会考虑超越简单属性的多种因素。为了捕捉这种复杂性，我们引入了对话式销售（CSALES），这是一个新颖的任务，在统一的对话框架内整合了偏好挖掘、推荐和说服。为了支持现实且系统的评估，我们提出了CSUSER，一种评估协议，该协议基于真实世界行为数据，通过建模细粒度用户画像以进行个性化互动，使用基于LLM的用户模拟器。我们还提出了CSI，一种对话式销售智能体，它通过对话主动推断上下文用户画像并策略性地选择行动。综合实验表明，CSI在多样化用户画像中显著提升了推荐成功率和说服效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
