让AI理解用户身份与情境，自动给出个性化建议。
PersoPilot: An Adaptive AI-Copilot for Transparent Contextualized Persona Classification and Personalized Response Generation
- 将用户身份与当前情境结合分析，动态生成回应
- 支持用户自然语言表达偏好，系统实时反馈
- 适合需要精准个性服务的场景，如客服、推荐
理解并分类用户身份对实现有效个性化至关重要。仅拥有身份信息不足以发挥价值，必须结合具体情境，才能提供精准且有意义的服务。现有系统常将身份与情境分开处理，难以生成细腻自适应的交互。为此，我们提出 PersoPilot——一个智能代理型AI协作者，融合身份理解与情境分析，服务于终端用户和分析师。终端用户通过透明可解释的聊天界面，以自然语言表达偏好、请求建议、获取任务导向的信息。分析师端则配备基于主动学习的标注助手，能随新标注数据持续优化分类模型，形成反馈闭环，支持定向服务推荐与动态个性化。该框架具备广泛适用性，可部署于多种个性化服务场景。
原文摘要 · Abstract (English)
Understanding and classifying user personas is critical for delivering effective personalization. While persona information offers valuable insights, its full potential is realized only when contextualized, linking user characteristics with situational context to enable more precise and meaningful service provision. Existing systems often treat persona and context as separate inputs, limiting their ability to generate nuanced, adaptive interactions. To address this gap, we present PersoPilot, an agentic AI-Copilot that integrates persona understanding with contextual analysis to support both end users and analysts. End users interact through a transparent, explainable chat interface, where they can express preferences in natural language, request recommendations, and receive information tailored to their immediate task. On the analyst side, PersoPilot delivers a transparent, reasoning-powered labeling assistant, integrated with an active learning-driven classification process that adapts over time with new labeled data. This feedback loop enables targeted service recommendations and adaptive personalization, bridging the gap between raw persona data and actionable, context-aware insights. As an adaptable framework, PersoPilot is applicable to a broad range of service personalization scenarios.
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