ConSearcher: Supporting Conversational Information Seeking in Online Communities with Member Personas
This addresses the challenge of improving conversational search for users browsing online communities, though it is incremental as it builds on existing LLM-powered tools.
The paper tackled the problem of conversational information seeking in online communities by proposing ConSearcher, an LLM-powered tool that uses dynamically generated member personas, which resulted in significantly higher information-seeking outcomes and user engagement compared to baselines in a study with 27 participants.
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.