PersoPilot: 一种适应性AI助手用于透明的上下文化人格分类和个性化响应生成
PersoPilot: An Adaptive AI-Copilot for Transparent Contextualized Persona Classification and Personalized Response Generation
AI总结：
PersoPilot是一种集成人格理解和上下文分析的AI助手，通过透明可解释的界面实现个性化服务推荐和适应性个性化。
摘要：
理解并分类用户人格对于提供有效的个性化服务至关重要。虽然人格信息提供了有价值的见解，但只有在上下文中进行关联，将用户特征与情境背景联系起来，才能充分发挥其潜力，从而实现更精确和有意义的服务提供。现有系统往往将人格和上下文视为独立输入，限制了其生成细致、适应性互动的能力。为了解决这一差距，我们提出了PersoPilot，一种代理式AI助手，将人格理解与上下文分析相结合，以支持终端用户和分析人员。终端用户通过透明、可解释的聊天界面进行交互，他们可以在自然语言中表达偏好，请求推荐，并接收针对其即时任务定制的信息。在分析人员方面，PersoPilot提供一个透明、基于推理的标记助手，整合了由主动学习驱动的分类过程，该过程能够随着时间推移通过新标记数据进行调整。这种反馈循环使服务推荐更加精准，并实现适应性的个性化，弥合了原始人格数据与可操作、情境感知洞察之间的差距。作为一种可扩展的框架，PersoPilot适用于广泛的服务个性化场景。
英文摘要：
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.
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。
