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

2026-03-20

AI Summary

This work proposes ConSearcher, a large language model (LLM)-based conversational search system designed to address the limitations of personalization and diversity in online community search. ConSearcher introduces dynamically generated member personas that are constructed in real time based on user queries, enabling the simulation of diverse virtual community members. Through a multi-perspective question-answering mechanism, the system supports interactive information exploration from varied viewpoints. A user study with 27 participants demonstrates that ConSearcher significantly outperforms baseline systems in both information retrieval effectiveness and user engagement. The findings also highlight potential risks associated with excessive personalization, offering new insights for the design of future community-oriented search systems.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Web Mining and Content Analysis: Community question answering

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.

Problem

• conversational search
• online communities
• information seeking
• member personas
• large language models

Innovation

• conversational search
• large language models
• member personas
• online communities
• information seeking