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 conversational search intents shaped by the open world. As a result, the system skews towards popular but generic suggestions.In this work, we uncover an untapped paradigm shift to shatter this harmful feedback loop: harnessing user "free will" through active user expressions. Unlike traditional recommendations, conversational search empowers users to bypass menus entirely through manually typed queries. The open-world intents in active queries hold the key to breaking this loop. However, incorporating them is non-trivial: (1) there exists an inherent distribution shift between active queries and formulated starters. (2) Furthermore, the "non-ID-able" nature of open text renders traditional item-based popularity statistics ineffective for large-scale industrial streaming training. To this end, we propose Passive-Active Bridge (PA-Bridge), a novel framework that employs an adversarial distribution aligner to bridge the distributional gap between passively recommended starters and active expressions. Moreover, we introduce a semantic discretizer to enable the deployment of popularity debiasing algorithms. Online A/B tests on our platform, demonstrate that PA-Bridge significantly boosts the Feature Penetration Rate by 0.54% and User Active Days

翻译：大型语言模型驱动的对话式搜索正将信息检索从被动关键词匹配转变为主动的开放式对话。在此背景下，对话开场白被广泛用于提供个性化查询推荐，帮助用户发起对话。传统上，推荐这些开场白依赖于封闭的"曝光-点击"循环。然而，这种反馈循环机制将系统禁锢在信息茧房中，加之数据稀疏性问题，使其无法捕捉由开放世界塑造的对话式搜索意图的动态特性。结果导致系统偏向于流行但泛泛的推荐。本研究中，我们揭示了一种未被开发的新范式来打破这种有害的反馈循环：通过利用用户的主动表达来驾驭其"自由意志"。与传统推荐不同，对话式搜索赋予用户通过手动输入查询完全绕过菜单的能力。主动查询中蕴含的开放世界意图正是打破这一循环的关键。但整合它们存在两大挑战：(1) 主动查询与预构开场白之间存在固有的分布偏移；(2) 开放文本的"不可标识化"特性使得传统的基于项目的流行度统计方法无法适用于大规模工业流式训练。为此，我们提出被动-主动桥接框架，该框架采用对抗性分布对齐器来弥合被动推荐开场白与主动表达之间的分布差异。此外，我们引入语义离散化器以支持流行度去偏算法的部署。在我们平台上的在线A/B测试表明，PA-Bridge显著提升了特征渗透率0.54%和用户活跃天数。