Bridging Passive and Active: Enhancing Conversation Starter Recommendation via Active Expression Modeling

arXiv:2605.05855v2 Announce Type: replace-cross Abstract: 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. Yet, this feedback loop mechanism traps the system in an echo chamber where, compounded by data sparsity, it fails to capture the dynamic nature of conversati

Source: arXiv cs.CL — read the full report at the original publisher.

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