An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

Lars Hertel; Gaurav Srivastava; Syed Ali Naqvi; Satyam Kumar; Yue Zhang; Borja Ocejo; Benjamin Zelditch; Adrian Englhardt; Hailing Cheng; Andy Hu; Antonio Alonso; Daming Li; Siddharth Dangi; Chen Zhu; Mingzhou Zhou; Wanning Li; Tao Huang; Fedor Borisyuk; Ganesh Parameswaran; Birjodh Singh Tiwana; Sriram Sankar; Qing Lan; Julie Choi; Souvik Ghosh

arXiv · 2602.12354

An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

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Abstract

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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Lars Hertel, Gaurav Srivastava, Syed Ali Naqvi, Satyam Kumar, Yue Zhang, Borja Ocejo, Benjamin Zelditch, Adrian Englhardt, Hailing Cheng, Andy Hu, Antonio Alonso, Daming Li, Siddharth Dangi, Chen Zhu, Mingzhou Zhou, Wanning Li, Tao Huang, Fedor Borisyuk, Ganesh Parameswaran, Birjodh Singh Tiwana, Sriram Sankar, Qing Lan, Julie Choi, Souvik Ghosh. 2026-02-12. An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking. https://arxiv.org/abs/2602.12354

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