An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
For large-scale social media platforms, this work demonstrates a practical sequential ranking model that improves engagement under strict production constraints.
LinkedIn deployed a transformer-based sequential recommender (Feed SR) to replace DCNv2 for Feed ranking, achieving +2.10% time spent and +3.52% engagement in online A/B tests over three months serving 1.2 billion members.
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.