An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918532023844864 |
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| author | Hertel, Lars Srivastava, Gaurav Naqvi, Syed Ali Kumar, Satyam Zhang, Yue Ocejo, Borja Zelditch, Benjamin Englhardt, Adrian Cheng, Hailing Hu, Andy Alonso, Antonio Li, Daming Dangi, Siddharth Zhu, Chen Zhou, Mingzhou Li, Wanning Huang, Tao Borisyuk, Fedor Parameswaran, Ganesh Tiwana, Birjodh Singh Sankar, Sriram Lan, Qing Choi, Julie Ghosh, Souvik |
| author_facet | Hertel, Lars Srivastava, Gaurav Naqvi, Syed Ali Kumar, Satyam Zhang, Yue Ocejo, Borja Zelditch, Benjamin Englhardt, Adrian Cheng, Hailing Hu, Andy Alonso, Antonio Li, Daming Dangi, Siddharth Zhu, Chen Zhou, Mingzhou Li, Wanning Huang, Tao Borisyuk, Fedor Parameswaran, Ganesh Tiwana, Birjodh Singh Sankar, Sriram Lan, Qing Choi, Julie Ghosh, Souvik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12354 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking Hertel, Lars Srivastava, Gaurav Naqvi, Syed Ali Kumar, Satyam Zhang, Yue Ocejo, Borja Zelditch, Benjamin Englhardt, Adrian Cheng, Hailing Hu, Andy Alonso, Antonio Li, Daming Dangi, Siddharth Zhu, Chen Zhou, Mingzhou Li, Wanning Huang, Tao Borisyuk, Fedor Parameswaran, Ganesh Tiwana, Birjodh Singh Sankar, Sriram Lan, Qing Choi, Julie Ghosh, Souvik Information Retrieval 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. |
| title | An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.12354 |