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
The proliferation of Large Language Models (LLMs) is driving a fundamental shift in information retrieval paradigms, necessitating more sophisticated methods for guiding user interaction beyond traditional keyword matching.
Improving conversation starter recommendation directly enhances the practical utility and adoption of LLM-driven conversational search, impacting how users extract information and interact with AI systems.
The proposed 'active expression modeling' moves beyond passive 'exposure-click' loops, enabling more dynamic, non-obvious, and personalized recommendations that better reflect the nuances of human conversation.
- · Conversational AI platforms
- · Search engine providers
- · AI/ML researchers
- · Legacy keyword search providers
More engaging and effective conversational search experiences for users.
Accelerated development of more adaptive and context-aware AI agents capable of initiating and guiding complex dialogues.
Potential for AI systems to proactively surface novel information or perspectives that users might not have explicitly sought.
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Read at arXiv cs.CL