LinkedIn rebuilt its content algorithm from scratch, replacing multiple patched systems with a unified AI-powered system. The feed now distributes content using interest-based signals rather than relying mainly on who users follow or connect with. It tracks a user’s professional journey over time to build an ongoing picture of interests. The algorithm also understands meaning, allowing posts to reach relevant audiences even when exact keywords are absent. Related posts help associate a brand or person with specific topics, improving delivery to the right audience over time. This compounding effect can give specialists more reach from the same posting effort.
"LinkedIn has undergone one of the most significant overhauls in its history. The platform rebuilt its content algorithm from scratch, moving from five separate systems patched together into a single, unified AI-powered brain. The most important shift is in how the feed distributes content. The old version showed you content primarily from people you already followed or connected with. The new system uses interest-based distribution to surface posts based on topics, not just account relationships."
"The system tracks what LinkedIn itself calls a user's "professional journey over time," building an ongoing picture of interests rather than reacting only to the last thing someone clicked. This mirrors the evolution already seen on YouTube, Facebook, and TikTok, where following someone no longer guarantees you see everything they publish."
"The algorithm also now understands meaning, not just keywords. A post about reducing churn can now reach someone searching for customer retention, even if those exact words don't appear anywhere in your content. The change means that specialists now have a meaningful advantage."
"A series of related posts trains the algorithm to associate you or your brand with a specific topic, and then serves your content to the right audience more reliably over time. This compounding effect means specialists stand to gain more reach from the same amount of posting effort."
#linkedin-algorithm #content-strategy #ai-recommendation-systems #audience-targeting #professional-interests
Read at Social Media Examiner
Unable to calculate read time
Collection
[
|
...
]