An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking

arXiv (Cornell University) 2026 Preprint Open access

Read full text
Authors: Lars Hertel, Gaurav Kumar Srivastava, Syed Ali Naqvi, Satyam Kumar, Yue Zhang, Borja Ocejo, Benjamin Zelditch, Adrian Englhardt, Hailing Cheng, Andy Hu, Antonio Alonso, Daming Li
+12 more Siddharth Dangi, Chen Zhu, Mingzhou Zhou, Wanning Li, Tao Huang, Fedor Borisyuk, Ganesh Parameswaran, Birjodh Tiwana, Sriram Sankar, Qing Lan, Julie Y. Choi, Souvik Ghosh
DOI: 10.48550/arxiv.2602.12354
Cited by: 0

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.

Fields of Study & Subjects

Recommender Systems and Techniques Information Retrieval and Search Behavior Advanced Bandit Algorithms Research

Publication details

Journal / source
arXiv (Cornell University)
Publication date
12 February 2026
Language
EN
Document type
Preprint