What does the LinkedIn algorithm work based on?

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The LinkedIn algorithm is complex, but its core logic can be deduced from experience and official hints. At its heart, it prioritizes **relevance and engagement** on two levels: first, the *individual profile quality* (complete information, work history, follower count), and second, the *interactive behavior* of your target audience. Of these factors, **profile completeness** carries about 30–40% weight—LinkedIn rewards profiles that clearly showcase work experience, skills, and recommendations with higher visibility. However, this only applies if the data is backed by *active* engagement. **Interactions** (likes, comments, shares) make up the bulk of the ranking, especially when they come from *highly relevant contacts* (e.g., industry experts or direct connections). Here, quality trumps quantity: a thoughtful comment on your CEO’s post will carry more weight than 10 superficial likes from random connections. **Keywords** play a minor role unless they’re embedded in an *active* context—such as a well-researched article or a comment using industry terms. The algorithm doesn’t use them primarily for direct ranking optimization but for contextual analysis (e.g., "Java Developer" in a tech discussion). Recent features like "Collaborative Articles" or newsletters also show that LinkedIn increasingly favors *long-term thought leadership* over short-term clicks: those who consistently share valuable content—regardless of keyword density—are rewarded in the long run.