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 LinkedIn scale. Feed-SR is currently the primary member experience on LinkedIn's Feed and shows significant improvements in member engagement (+2.10% time spent) 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信息流助力全球专业人士大规模发现相关内容、建立联系并分享知识。本文提出Feed序列化推荐系统（Feed-SR），这是一种基于Transformer的LinkedIn信息流序列化排序模型，它取代了基于DCNv2的排序器，并满足严格的生产约束。我们详细阐述了实现LinkedIn规模部署所需的建模选择、训练技术和服务优化方案。Feed-SR目前是LinkedIn信息流上的核心用户体验，在线A/B测试显示，相较于现有生产模型，其在用户参与度（+2.10%停留时长）方面取得显著提升。我们还阐述了采用替代性序列化及基于LLM的排序架构的部署经验，并说明了Feed-SR为何能实现在线指标与生产效率的最佳平衡。