Short-video platforms now expose clickable search entries beneath the video player, enabling users to easily express content-induced search intent. However, conventional query recommendation systems on short-video platforms suffer from latency constraints and objective misalignment, while recent generative approaches struggle with noisy content-side metadata and preference drift. To address these issues, we propose OneBar, an end-to-end generative framework for real-time query recommendation for E-Commerce video feeds. OneBar features three key innovations: (1) a collaborative-multimodal intent grounding module that fuses multimodal video understanding and behavior-derived collaborative anchors; (2) a Unified End-to-End architecture equipped with a prompt-compression mechanism for efficient online serving; and (3) a progressive preference learning strategy for efficient preference-internalization, which internalizes hierarchical behavior preferences into the generative policy, eliminating the need for a separately trained reward model. Compared with online base, OneBar increases Query Exposure by 16.91\% and Query Click by 18.68\%, while maintaining a slight Query CTR gain of 0.19\%. The additional search traffic further contributes to 20.36\% more guided orders and 21.67\% higher GMV.

翻译：短时视频平台现在在播放器下方展示可点击的搜索入口，这使得用户可以便捷地表达由视频内容诱发的搜索意图。然而，短时视频平台上的传统查询推荐系统受限于延迟约束与目标错配问题，而新兴的生成式方法则在处理含噪内容侧元数据和偏好漂移时面临挑战。为解决上述问题，我们提出了OneBar——一个面向电商视频流实时查询推荐的端到端生成式框架。OneBar包含三项关键创新：(1) 协同多模态意图对齐模块，融合多模态视频理解与行为派生协同锚点；(2) 配备提示压缩机制的统一端到端架构，以实现高效的在线服务；(3) 渐进式偏好学习策略，用于高效的内在化偏好吸收，该策略将层次化行为偏好内化至生成策略中，从而无需单独训练奖励模型。相较于在线基线，OneBar使查询展示量提升了16.91%，查询点击量提升了18.68%，同时保持了查询点击率（CTR）0.19%的微小增长。额外的搜索流量进一步带动引导订单量增长20.36%，商品交易总额（GMV）增长21.67%。