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.

翻译：LinkedIn Feed使全球专业人士能够大规模发现相关内容、建立联系并分享知识。我们提出Feed序列推荐模型（Feed SR），这是一种基于Transformer的序列排序模型，用于替代原有的DCNv2排序器，并满足严格的生产部署约束。本文详细介绍了实现12亿会员规模部署所需的模型设计、训练技术与服务优化方案。Feed SR已承担LinkedIn Feed大部分流量超过三个月，在线A/B测试显示，与现有生产模型相比，该模型显著提升了用户参与度（停留时长+2.10%，点赞、评论或转发+3.52%）。我们还描述了替代性序列与基于LLM的排序架构的部署经验，并论证了为何Feed SR在线上指标与生产效率之间实现了最佳平衡。