ConSearcher: Supporting Conversational Information Seeking in Online Communities with Member Personas

TL;DR

ConSearcher is an LLM-powered tool that uses dynamically generated member personas to enhance conversational search in online communities, improving information outcomes and user engagement.

Contribution

It introduces a novel approach of using member personas generated from user queries to support conversational information seeking in online communities.

Findings

ConSearcher significantly improved information-seeking outcomes.

Users showed higher engagement with ConSearcher.

Concerns about over-personalization were identified.

Abstract

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.