Yang Gu
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Streaming Trends: A Low-Latency Platform for Dynamic Video Grouping and Trending Corpora Building
Scott Wang
Caroline Zhou
Ashkan Norouzi Fard
Yongzhe Wang
Yaping Zhang
Li Zhang
Mingyan Gao
Qiao Zhang
2025
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This paper presents Streaming Trends, a real-time system deployed on a short-form videos platform that enables dynamic content grouping, tracking videos from upload to their identification as part of a trend. Addressing the latency inherent in traditional batch processing for short-form video, Streaming Trends utilizes online clustering and flexible similarity measures to associate new uploads with relevant groups in near real-time. The system combines online processing for immediate updates triggered by uploads and seed queries with offline processes for similarity modeling and cluster quality maintenance. By facilitating the rapid identification and association of trending videos, Streaming Trends significantly enhances content discovery and user engagement on the YouTube Shorts platform.
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