Source link : https://tech365.info/how-linkedin-changed-5-feed-retrieval-techniques-with-one-llm-mannequin-at-1-3-billion-user-scale/
LinkedIn’s feed reaches greater than 1.3 billion members — and the structure behind it hadn’t saved tempo. The system had collected 5 separate retrieval pipelines, every with its personal infrastructure and optimization logic, serving totally different slices of what customers would possibly need to see. Engineers on the firm spent the final yr tearing that aside and changing it with a single LLM-based system. The end result, LinkedIn says, is a feed that understands skilled context extra exactly and prices much less to run at scale.
The redesign touched three layers of the stack: how content material is retrieved, the way it’s ranked, and the way the underlying compute is managed. Tim Jurka, vice chairman of engineering at LinkedIn, informed VentureBeat the crew ran a whole bunch of exams over the previous yr earlier than reaching a milestone that, he says, reinvented a big chunk of its infrastructure.
“Starting from our entire system for retrieving content, we’ve moved over to using really large-scale LLMs to understand content much more richly on LinkedIn and be able to match it much in a much more personalized way to members,” Jurka mentioned. “All the way to how we rank content, using really, really large sequence models, generative recommenders, and combining that end-to-end system to make things much more relevant and meaningful for members.”
One feed, 1.3 billion members
The core problem, Jurka mentioned, is two-sided: LinkedIn has to match members’…
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Author : tech365
Publish date : 2026-03-17 06:32:00
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