AI 中文总结
针对数字出版商需为文本文章匹配相关视频的需求，提出基于LLMs和文本嵌入的上下文视频匹配系统，已在Dailymotion部署并获数百家出版商采用，提升了用户参与度与体验。
AI 中文摘要
数字出版商面临着管理海量内容目录的挑战，将相关视频有效嵌入文本文章的能力对盈利和用户留存都至关重要。然而，手动选择对于大型出版商而言不切实际，尤其是在梳理自身庞大的视频库或整个全球Dailymotion目录时。本文提出了“上下文视频匹配”系统，该系统可自动为文本密集型网页和文章匹配相关视频。通过利用大型语言模型（LLMs）和文本嵌入，我们为出版商提供了一种可扩展的解决方案，使其能高效地将视频内容与文章结合。我们详细讨论了该系统在Dailymotion生产环境中的动机、架构、评估和部署情况。自推出以来，该系统已被数百家出版商采用，显著提升了用户参与度，并用高度相关的视频内容丰富了用户体验。
英文摘要
As digital publishers face the challenge of managing massive content catalogs, the ability to effectively embed relevant video within text-based articles has become critical for both monetization and user retention. However, manual selection is impractical for large scale publishers, especially when navigating their own extensive video libraries or the entire global Dailymotion catalog. In this paper, we present the "Contextual Video Matching" system, a solution that automatically matches relevant videos with text-heavy web pages and articles. By leveraging Large Language Models (LLMs) and textual embeddings, we provide a scalable solution for publishers to efficiently combine video content with their articles. We discuss in detail the motivations, architecture, evaluations, and deployment of this system within Dailymotion's production environment. Since its launch, the system has been adopted by hundreds of publishers, significantly increasing user engagement and enriching user experiences with highly relevant video content.
