OneBar实时生成电商短视频搜索推荐，提升用户点击与转化。
OneBar: An End-to-End Content-Grounded Generative Query Recommendation Framework for E-Commerce Video Feeds
- 融合多模态视频理解与用户行为数据，精准捕捉搜索意图
- 查询曝光提升16.91%，点击率提高18.68%，带动20.36%更多订单
- 无需额外奖励模型，高效内化用户偏好，适合高并发推荐场景
短视频平台现可在视频播放器下方展示可点击的搜索入口，帮助用户表达由内容激发的搜索意图。然而，传统查询推荐系统存在延迟高、目标错位问题，而现有生成式方法又受制于噪声内容元数据和偏好漂移。为此，我们提出OneBar，一个面向电商短视频流的端到端生成式查询推荐框架。其三大创新包括：(1) 协同多模态意图定位模块，融合多模态视频理解与行为衍生的协同锚点；(2) 统一端到端架构，配备提示压缩机制，支持高效在线服务；(3) 渐进式偏好学习策略，将层级化行为偏好内化至生成策略中，无需独立训练奖励模型。相比线上基线，OneBar使查询曝光提升16.91%，点击率提高18.68%，查询点击率仅微增0.19%。新增搜索流量进一步带来20.36%的引导订单与21.67%更高的GMV。
原文摘要 · Abstract (English)
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.
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