When I think about how often I scroll through LinkedIn, I’m excited to share that the platform is launching a cutting-edge AI-powered feed ranking system. It’s designed to analyze what we post, read, and engage with, thanks to large language models and advanced GPUs. This innovation aims to provide more personalized content updates for its vast user base of 1.3 billion.
Why this matters to me. Understanding LinkedIn’s content surfacing process can be a game-changer for anyone wanting their posts—or their brand’s—to gain visibility. The focus is on what’s relevant and engaging within our network. As LinkedIn Tweaked their system, posts that show expertise and contribute to trending professional topics have a better chance to go viral, regardless of our existing connections.
What’s under the hood. LinkedIn has revamped its feed recommendation mechanism using large language models and sophisticated transformer models, all powered by GPU infrastructure. The overhaul targets two key functions: the retrieval and ranking of relevant posts in our feeds.
Unified retrieval system. One of the most intriguing aspects for me is how LinkedIn has consolidated its discovery processes into a single model powered by LLMs (large language models). Previously, posts could come from various sources such as network activity and trending topics. Now, LinkedIn uses LLM-generated embeddings to interpret post content and align it with our professional interests.
For instance, by engaging with posts about small modular reactors, I might see content linked to renewable energy or other related fields, even if they use different terminology.
Ranked by your interests. Once posts are retrieved, LinkedIn ranks them utilizing a transformer-based sequential model. Instead of looking at posts individually, the model examines patterns in my past interactions, including likes, comments, and the time I spent viewing content.
This helps LinkedIn adapt to my evolving professional interests and recommend content that aligns with these shifts.
System performance and architecture. Powered by a GPU infrastructure that processes millions of posts, this system keeps our feeds fresh.
LinkedIn reports that this system can refresh content embeddings in mere minutes and retrieve suitable candidates in under 50 milliseconds.
Enhancing feed quality and authenticity. LinkedIn has also announced updates aimed at boosting content quality:
- Addressing automated engagement. They’ve started cracking down on tools that automate comments or use engagement pods to fake discussions. LinkedIn clarifies these violate platform policies and devalue genuine interactions.
- Cutting down on engagement bait and generic content. The platform will deprioritize content designed solely to provoke comments or clicks—such as posts begging for comments to inflate reach, irrelevant video-text pairings, and regurgitated thought-leadership content.
- Helping newcomers customize their feeds faster. New users can now utilize the “Interest Picker” during signup to select topics of interest, whether it be leadership, career growth, or job-seeking skills, ensuring relevance from day one.
Inspired by this post on Search Engine Land.
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