对话代理的IceBreaker：通过个性化开场白打破第一消息障碍
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters
AI总结：
本文提出IceBreaker，通过生成个性化开场白帮助对话代理克服用户初始对话时的障碍，提升用户活跃度和点击率。
摘要：
对话代理，如ChatGPT和Doubao，已成为数十亿用户的日常助手。为了进一步提高参与度，这些系统正从被动响应者发展为主动伴侣。然而，现有努力集中在正在进行的对话中激活，而忽略了现实中的关键瓶颈。在对话启动阶段，用户可能有模糊的需求但没有明确的查询意图，造成对话开始前的障碍。为此，我们引入对话开场生成：生成个性化开场白以引导用户进入对话。然而，与对话阶段不同，启动必须在冷启动时刻没有明确用户意图的情况下进行。为此，我们提出IceBreaker，将人类的破冰行为视为两步握手：(i) 通过会话摘要中的共鸣意识兴趣萃取来激发共鸣，捕获触发兴趣；(ii) 通过以互动为导向的开场生成，优化个性化偏好对齐和自我强化循环以最大化参与度。在世界上最大的对话代理产品上的在线A/B测试显示，IceBreaker提高了用户活跃天数+0.184%和点击率+9.425%，并且已部署到生产环境中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
