@inproceedings{zheng-etal-2026-icebreaker,
title = "{I}ce{B}reaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters",
author = "Zheng, Hongwei and
Wu, Weiqi and
Wang, Zhengjia and
Jiang, Guanyu and
Li, Haoming and
Wu, Tianyu and
Zhu, Yongchun and
Chen, Jingwu and
Zhang, Feng",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.16/",
pages = "230--241",
ISBN = "979-8-89176-394-4",
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 +1.84{\textperthousand} and click-through rate by +94.25{\textperthousand}, and has been deployed in production."
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<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 +1.84\textperthousand and click-through rate by +94.25\textperthousand, and has been deployed in production.</abstract>
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%0 Conference Proceedings
%T IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters
%A Zheng, Hongwei
%A Wu, Weiqi
%A Wang, Zhengjia
%A Jiang, Guanyu
%A Li, Haoming
%A Wu, Tianyu
%A Zhu, Yongchun
%A Chen, Jingwu
%A Zhang, Feng
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zheng-etal-2026-icebreaker
%X 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 +1.84\textperthousand and click-through rate by +94.25\textperthousand, and has been deployed in production.
%U https://aclanthology.org/2026.acl-industry.16/
%P 230-241
Markdown (Informal)
[IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters](https://aclanthology.org/2026.acl-industry.16/) (Zheng et al., ACL 2026)
ACL
- Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, and Feng Zhang. 2026. IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 230–241, San Diego, California, USA. Association for Computational Linguistics.