发表机构
Google（谷歌）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个基于多臂老虎机的全自动端到端框架，在十亿级短视频平台上动态选择最优缩略图，通过图像先验降低探索成本，显著提升用户发现与参与指标。
AI 中文摘要
本文介绍了一个在全球主要短视频平台上以$O(B)$规模部署的实时缩略图优化系统。与创作者精心策划自定义缩略图的传统长视频内容不同，相当比例的短视频在发布时没有人工选择的封面图。为解决这一未策划的内容库，我们提出了一种全自动、端到端的框架，用动态、数据驱动的选择取代静态默认帧，覆盖数十亿视频。据我们所知，这是首个公开证明在线多臂老虎机框架成功部署于$O(B)$规模、用于未策划短视频发现的工作。我们的解决方案将多阶段候选生成流程与低延迟服务基础设施相结合。通过利用深度视觉质量模型得出的图像特定先验来初始化探索框架，系统在服务时最小化探索成本并动态提供最优缩略图。全球部署表明，在核心用户发现和参与度指标上取得了统计显著的改进。
英文摘要
This paper introduces a real-time thumbnail optimization system deployed at a global $O(B)$ scale on a major short-form video platform. Unlike traditional long-form content, where custom thumbnails are heavily curated by creators, a considerable fraction of short-form videos are published without human-selected artwork. To address this uncurated corpus, we present a fully automated, end-to-end framework that replaces static default frames with dynamic, data-driven selections across billions of videos. To the best of our knowledge, this is the first published work demonstrating an online Multi-Armed Bandit framework successfully deployed at an $O(B)$ scale for uncurated short-form video discovery. Our solution pairs a multi-stage candidate generation pipeline with a low-latency serving infrastructure. By initializing the exploration framework with image-specific priors derived from a deep visual quality model, the system minimizes exploration costs and dynamically serves optimal thumbnails at serving time. Global deployment demonstrates statistically significant improvements in core user discovery and engagement metrics.
