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.

翻译：理解并分类用户角色对于实现有效个性化至关重要。虽然角色信息提供了有价值的洞察，但其全部潜力只有在情境化后才能真正发挥——即将用户特征与情境背景相关联，从而实现更精准、更有意义的服务提供。现有系统通常将角色和情境视为独立输入，限制了其生成细致入微、自适应交互的能力。为弥补这一不足，我们提出了PersoPilot，一种智能AI副驾驶，它将角色理解与情境分析相结合，以同时支持终端用户和分析师。终端用户通过一个透明、可解释的聊天界面进行交互，在此界面中，他们可以用自然语言表达偏好、请求推荐，并获取针对其即时任务定制的信息。在分析师端，PersoPilot提供了一个透明的、基于推理的标注助手，该助手与一个主动学习驱动的分类流程相集成，该流程能随着新标注数据的加入而持续自适应调整。这一反馈循环实现了有针对性的服务推荐和自适应个性化，弥合了原始角色数据与可操作的、情境感知的洞察之间的差距。作为一个可适应框架，PersoPilot适用于广泛的服务个性化场景。