LinkedIn用Transformer模型提升信息流推荐，用户互动显著提升。
An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
- 采用Transformer架构替代原有模型，支持大规模序列化内容推荐。
- 上线后用户停留时间增2.10%，点赞评论转发率升3.52%。
- 适合追求高精度与生产效率的工业级推荐系统开发者参考。
LinkedIn信息流为全球专业人士提供内容发现、连接建立和知识分享的平台。本文介绍面向信息流推荐的序列化推荐模型Feed SR，该模型基于Transformer，取代原有的DCNv2排名模型，并满足严格的生产约束。我们详细阐述了建模选择、训练技术及服务优化策略，使模型可支撑12亿用户的规模。自上线以来，Feed SR已持续服务多数信息流流量超过三个月，在线上A/B测试中相比现有模型显著提升用户参与度：人均停留时间增加2.10%，点赞、评论或转发行为提升3.52%。同时，我们对比了其他序列化与基于大语言模型的排序架构，证明Feed SR在在线指标与生产效率之间取得了最佳平衡。
原文摘要 · Abstract (English)
LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed SR), a transformer-based sequential ranking model for LinkedIn Feed that replaces a DCNv2-based ranker and meets strict production constraints. We detail the modeling choices, training techniques, and serving optimizations that enable deployment at a scale of 1.2 billion members. Feed SR has been serving the majority of LinkedIn's Feed traffic for over three months and shows significant improvements in member engagement (+2.10% time spent, +3.52% like, comments, or reshares) in online A/B tests compared to the existing production model. We also describe our deployment experience with alternative sequential and LLM-based ranking architectures and why Feed SR provided the best combination of online metrics and production efficiency.
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