基于真实工作场景，构建任务导向的信息请求分类体系。
Rules, Resources, and Restrictions: A Taxonomy of Task-Based Information Request Intents
- 通过访谈机场服务人员，提炼出任务驱动的查询意图框架。
- 提出涵盖规则、资源、限制三类的核心意图分类体系。
- 适合研究智能助手、任务型搜索与LLM交互设计的学者。
理解与分类查询意图可提升检索效果，使结果更契合用户查询背后的动机。然而，现有意图分类体系多源于系统日志数据，仅捕捉孤立的信息需求，未能充分考虑任务上下文。随着大型语言模型（LLMs）的应用扩展，用户期望已从简单问答转向全面的任务支持，如购物决策或旅行规划。但当前的LLMs仍难以完整解析复杂多维的任务。为弥补这一差距，本文主张采用更强烈的任务导向视角来定义查询意图。基于对机场信息工作人员的扎根理论访谈研究，我们提出了一个任务导向的信息请求意图分类体系，弥合了传统以查询为中心的方法与人工智能驱动的任务导向搜索之间日益增长的需求鸿沟。
原文摘要 · Abstract (English)
Understanding and classifying query intents can improve retrieval effectiveness by helping align search results with the motivations behind user queries. However, existing intent taxonomies are typically derived from system log data and capture mostly isolated information needs, while the broader task context often remains unaddressed. This limitation becomes increasingly relevant as interactions with Large Language Models (LLMs) expand user expectations from simple query answering toward comprehensive task support, for example, with purchasing decisions or in travel planning. At the same time, current LLMs still struggle to fully interpret complex and multifaceted tasks. To address this gap, we argue for a stronger task-based perspective on query intent. Drawing on a grounded-theory-based interview study with airport information clerks, we present a taxonomy of task-based information request intents that bridges the gap between traditional query-focused approaches and the emerging demands of AI-driven task-oriented search.
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