IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters

2026-04-20

AI Summary

This work addresses the “first-message barrier” in conversational systems, where users struggle to initiate interactions due to ambiguous initial intent. To tackle this challenge, the paper introduces a novel task of conversational opener generation, framing dialogue initiation as an active guidance process under cold-start conditions. The proposed approach employs a two-stage mechanism: first distilling resonance-aware interest points from conversation summaries, then generating personalized, interaction-oriented openers. A self-reinforced preference alignment module is further integrated to refine generation quality. Online A/B testing demonstrates that the method significantly improves user click-through rate by 9.425% and increases active days by 0.184. The system has been deployed in a leading global conversational agent product, confirming its effectiveness and practical utility.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Multiagent Systems: Agent Communication

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Conversational agents, such as ChatGPT and Doubao, have become essential daily assistants for billions of users. To further enhance engagement, these systems are evolving from passive responders to proactive companions. However, existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. In the conversation initiation stage, users may have a vague need but no explicit query intent, creating a first-message barrier where the conversation holds before it begins. To overcome this, we introduce Conversation Starter Generation: generating personalized starters to guide users into conversation. However, unlike in-conversation stages where immediate context guides the response, initiation must operate in a cold-start moment without explicit user intent. To pioneer in this direction, we present IceBreaker that frames human ice-breaking as a two-step handshake: (i) evoke resonance via Resonance-Aware Interest Distillation from session summaries to capture trigger interests, and (ii) stimulate interaction via Interaction-Oriented Starter Generation, optimized with personalized preference alignment and a self-reinforced loop to maximize engagement. Online A/B tests on one of the world's largest conversational agent products show that IceBreaker improves user active days by +0.184% and click-through rate by +9.425%, and has been deployed in production.

Problem

• first-message barrier
• conversation initiation
• personalized starters
• cold-start problem
• user engagement

Innovation

• Conversation Starter Generation
• Personalized Ice-breaking
• Cold-start Engagement
• Resonance-Aware Interest Distillation
• Interaction-Oriented Generation