自动为文章匹配相关视频，提升用户留存与变现
Every Article Deserves a Video: Contextual Video Matching for Digital Publishers
- 用大模型和文本嵌入技术实现文章与视频的智能匹配
- 上线后被数百家出版商采用，显著提升用户参与度
- 适合内容平台、媒体公司快速增强图文体验
数字出版商面临海量内容库管理挑战，将相关视频嵌入文字类文章对变现和用户留存至关重要。然而，大规模场景下人工选择不现实，尤其当需在自有视频库或全球Dailymotion目录中筛选时。本文提出「上下文视频匹配」系统，通过大型语言模型（LLMs）与文本嵌入技术，自动为文本密集型网页和文章匹配相关视频。我们详细阐述了系统的设计动机、架构、评估及在Dailymotion生产环境中的部署。自上线以来，该系统已被数百家出版商采用，显著提升用户参与度，并通过高度相关的视频内容丰富了用户体验。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。