How does the LinkedIn algorithm work in 2026?

2,000

candidate posts from outside a member's network are picked from a pool of hundreds of millions for its suggested content, before ranking decides their order, in a 2025 LinkedIn paper

What happens between you posting and someone seeing it?

The feed works in two broad steps, retrieval and then ranking. Retrieval gathers candidates from several places, balancing posts from your network and the people you follow with suggested posts from further afield, as LinkedIn's engineering blog describes.

LinkedIn's 2025 paper on feed retrieval covers the suggested part. It narrows hundreds of millions of posts to about 2,000 for each member, using a language model that reads posts and members as text, so it can match a post to someone outside your network whose interests fit.

Ranking then puts the candidates in order. In a 2026 paper, LinkedIn's engineers describe a transformer model that learns from the sequence of things a member has engaged with recently. They say it has served most feed traffic for more than three months, built for 1.2 billion members, and in online tests it raised time spent by 2.10 percent.

What does LinkedIn say it rewards and holds back?

LinkedIn set this out in plain terms in its March 2026 feed announcement. It wants posts that offer genuine insight, practical ideas and considered points of view, matched to each member's interests whether the author is a connection or a stranger. It draws on what members choose to share, such as industry, skills, experience and location, along with how their engagement changes over time.

The same announcement names what it wants less of. That includes engagement bait such as prompts to comment a word to agree, videos that don't match the text of their post, comment automation, engagement pods, and recycled or click-driven posts. LinkedIn says the aim is a feed shaped by relevance to each person rather than a contest of popularity.

Why does what a post is about matter more now?

Because the models increasingly read the post itself. LinkedIn's March 2026 engineering blog says its new ranking system, built on large language models, better understands what a post is actually about and how it relates to a member's interests and career goals. What it reads includes the post text and the author's name, headline, company and industry.

That is a shift from the previous generation. LinkedIn's 2024 LiRank paper describes ranking models that used large sets of engineered features and lifted feed sessions by 0.5 percent, while the 2026 ranking model uses about 80 percent fewer features. A clear point aimed at a clear audience is easier to match with the right readers.

What does that mean for your own posts?

Write about what you know, for a reader you can picture, and say it plainly enough that the topic is obvious from the first lines. Keep your headline, experience and skills current, since LinkedIn names them among the signals it reads. Skip tricks aimed at the old system, from engagement bait to pods, because those are exactly what it says it is cutting.

Then give it time. One post tells you little, while a steady rhythm over a few months shows which topics find their readers. For a rhythm that holds, see how often a founder should post, and for keeping AI drafts in your own words, see making AI-written posts sound like you.