Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.

翻译：对话搜索已演变为一种新型信息检索范式，标志着从传统搜索系统向与智能搜索代理交互对话的转变。这种变革尤其影响探索式信息搜寻场景，其中对话搜索系统能够引导用户发现陌生领域。在此类场景中，用户常因缺乏背景知识而难以表达信息目标。对话式界面可通过激发信息需求并缩小搜索空间提供辅助。然而，由于信息搜寻行为的复杂性，设计用于信息检索的对话式界面仍面临重大挑战。尽管先前研究通过用户实验为系统设计提供实证依据，但现有研究大多局限于明确界定的搜索任务或已知领域，本质上探索性较弱。因此，我们开展了一项实验室研究，探究用户在未知信息空间中进行开放式导航的搜索行为。该研究包含26名受试者，其搜索行为被限制在文本聊天界面中。基于收集的对话记录，我们运用统计分析和过程挖掘技术，揭示了跨五个不同领域的通用信息搜寻模式。我们不仅识别出使用户能够发现领域知识的核心对话行为及其相互关系，还提出了对话搜索系统的设计建议。