AI RESEARCH
Bridging Passive and Active: Enhancing Conversation Starter Recommendation via Active Expression Modeling
arXiv CS.CL
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ArXi:2605.05855v1 Announce Type: cross Large Language Model (LLM)-driven conversational search is shifting information retrieval from reactive keyword matching to proactive, open-ended dialogues. In this context, Conversation Starters are widely deployed to provide personalized query recommendations that help users initiate dialogues. Conventionally, recommending these starters relies on a closed "exposure-click" loop.