用大模型提升短视频平台趋势发现的准确性和时效性
LLM-Enhanced Topical Trend Detection at Snapchat
- 融合多模态提取与时间序列突增检测，结合大模型整合信息
- 六个月内人工评估显示高精度，已在全球规模上线应用
- 适合关注社交平台内容推荐与趋势追踪的研究者和工程师
在社交媒体平台上大规模自动识别话题趋势既具挑战性又至关重要。本文提出一个面向Snapchat——全球最大的短视频平台之一——的端到端话题趋势检测系统。该系统整合多模态话题提取、时间序列突增检测以及大模型驱动的趋势合并与丰富机制，实现高效精准的趋势发现。据我们所知，这是首个在生产环境中部署的短视频平台话题趋势检测系统。持续六个月的离线人工评估表明，系统能高精度识别有意义的趋势。目前该系统已在全局规模上线，并应用于内容排序与搜索等下游场景，显著提升了内容新鲜度和用户体验。
原文摘要 · Abstract (English)
Automatic detection of topical trends at scale is both challenging and essential for maintaining a dynamic content ecosystem on social media platforms. In this work, we present a large-scale system for identifying emerging topical trends on Snapchat, one of the world's largest short-video social platforms. Our system integrates multimodal topic extraction, time-series burst detection, and LLM-based consolidation and enrichment to enable accurate and timely trend discovery. To the best of our knowledge, this is the first published end-to-end system for topical trend detection on short-video platforms at production scale. Continuous offline human evaluation over six months demonstrates high precision in identifying meaningful trends. The system has been deployed in production at global scale and applied to downstream surfaces including content ranking and search, driving measurable improvements in content freshness and user experience.
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。