Paper
ConSearcher: Supporting Conversational Information Seeking in Online Communities with Member Personas
Authors
Shiwei Wu, Xinyue Chen, Yuheng Liu, Xingbo Wang, Qingyu Guo, Longfei Chen, Chuhan Shi, Zhenhui Peng
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.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.19747v1</id>\n <title>ConSearcher: Supporting Conversational Information Seeking in Online Communities with Member Personas</title>\n <updated>2026-03-20T08:31:51Z</updated>\n <link href='https://arxiv.org/abs/2603.19747v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.19747v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-03-20T08:31:51Z</published>\n <arxiv:comment>25 pages, 7figures</arxiv:comment>\n <arxiv:primary_category term='cs.HC'/>\n <author>\n <name>Shiwei Wu</name>\n </author>\n <author>\n <name>Xinyue Chen</name>\n </author>\n <author>\n <name>Yuheng Liu</name>\n </author>\n <author>\n <name>Xingbo Wang</name>\n </author>\n <author>\n <name>Qingyu Guo</name>\n </author>\n <author>\n <name>Longfei Chen</name>\n </author>\n <author>\n <name>Chuhan Shi</name>\n </author>\n <author>\n <name>Zhenhui Peng</name>\n </author>\n </entry>"
}