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 by 0.04%.

翻译：大型语言模型驱动的对话式搜索正在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，对话启动器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，推荐这些启动器依赖于封闭的"曝光-点击"循环。然而，这种反馈循环机制使系统陷入信息茧房，加之数据稀疏性，导致其无法捕捉开放世界塑造的对话式搜索意图的动态特性。因此，系统倾向于推荐流行但泛泛的选项。本文揭示了一种打破此有害反馈循环的未开发范式转变：通过用户主动表达利用其"自由意志"。与传统推荐不同，对话式搜索允许用户通过手动输入查询完全绕过菜单。主动查询中的开放世界意图是打破此循环的关键。然而，融入它们并非易事：(1) 主动查询与结构化启动器之间存在固有的分布偏移。(2) 此外，开放文本的"非身份化"特性使得传统的基于物品的流行度统计方法无法应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，该框架采用对抗分布对齐器来弥合被动推荐启动器与主动表达之间的分布差距。此外，我们引入语义离散器以支持流行度去偏算法的部署。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0.04%。