Many people browse online communities to learn from others' experiences and opinions, e.g., for constructing travel plans. Conversational search powered by large language models (LLMs) could ease this information-seeking task, but it remains under-investigated within the online community. In this paper, we first conducted an exploratory study (N=10) that indicated the helpfulness of a classic conversational search tool and identified room for improvement. Then, we proposed ConSearcher, an LLM-powered tool with dynamically generated member personas based on user queries to facilitate conversational search in the community. In ConSearcher, users can clarify their interests by checking what a simulated member similar to them may ask and get responses from diverse members' perspectives. A within-subjects study (N=27) showed that compared to two conversational search baselines, ConSearcher led to significantly higher information-seeking outcome and user engagement but raised concerns about over-personalization. We discuss implications for supporting conversational information seeking in online communities.

翻译：许多用户浏览在线社区，以从他人的经验和观点中学习，例如制定旅行计划。由大型语言模型驱动的对话式搜索可简化此类信息搜索任务，但在在线社区场景下尚未得到充分研究。本文首先通过探索性研究（N=10）证实了经典对话式搜索工具的实用性，并识别出改进空间。随后，我们提出ConSearcher——一种基于用户查询动态生成成员角色画像的大语言模型驱动工具，旨在促进社区中的对话式搜索。在ConSearcher中，用户可通过查看与自身特征相似模拟成员可能提出的问题来明确兴趣，并从多成员视角获取回应。一项受试者内实验（N=27）表明，相较于两种对话式搜索基线，ConSearcher显著提升了信息搜索效果与用户参与度，但引发了关于过度个性化的担忧。本文讨论了支持在线社区中对话式信息搜索的启示。