LinkedIn's Feed uses a sequential transformer model that treats your engagement history as a narrative of your professional journey. It learns from your profile data, what you dwell on, what you comment on, and what you scroll past. Posts with high engagement across similar professionals are more likely to reach you, even from accounts you do not follow.
LinkedIn's Feed is designed for professional relevance rather than entertainment. The system uses multi-stage ranking — first retrieving candidates, then scoring them by predicted relevance — and balances your personal engagement history with broader signals about what professionals in your industry find valuable. Claims on this page are sourced from LinkedIn's Engineering Blog and official newsroom.
LinkedIn's Feed algorithm uses your profile data (industry, experience, skills, geography) combined with how you engage with content over time, including what you've read, liked, commented on, returned to, or scrolled past.
LinkedIn Engineering, 2024Dwell time — how long you pause on a post — is an embedded ranking signal alongside explicit actions like likes, comments, and shares in LinkedIn's model.
LinkedIn Engineering, 2024LinkedIn explicitly suppresses inauthentic engagement behaviours including comment automation, engagement pods, and unauthorised third-party tools.
LinkedIn, 2026LinkedIn actively reduces repetitive low-substance posts and engagement bait such as 'comment to agree' prompts or videos that do not match their text.
LinkedIn, 2026LinkedIn's Feed algorithm uses your profile data (industry, experience, skills, geography) combined with how you engage with content over time, including what you've read, liked, commented on, returned to, or scrolled past.
Source: LinkedIn Engineering, Engineering the next generation of LinkedIn's Feed (2024)
LinkedIn uses a sequential transformer model that treats your Feed interaction history as a sequence reflecting your professional journey, rather than treating interactions as independent decisions.
Source: LinkedIn Engineering, Engineering the next generation of LinkedIn's Feed (2024)
Dwell time — how long you pause on a post — is an embedded ranking signal alongside explicit actions like likes, comments, and shares in LinkedIn's model.
Source: LinkedIn Engineering, Engineering the next generation of LinkedIn's Feed (2024)
LinkedIn's Feed uses a two-stage system: Retrieval determines which posts reach the ranking stage; Ranking then determines what a member actually sees in their Feed.
Source: LinkedIn Engineering, Engineering the next generation of LinkedIn's Feed (2024)
Posts that many professionals find valuable are more likely to be shown to any individual member — LinkedIn uses percentile-bucketed engagement metrics as a popularity signal.
Source: LinkedIn Engineering, Engineering the next generation of LinkedIn's Feed (2024)
When a LinkedIn post is published or gains engagement, the system updates its ranking prompt within minutes to reflect the latest activity.
Source: LinkedIn Engineering, Engineering the next generation of LinkedIn's Feed (2024)
LinkedIn explicitly suppresses inauthentic engagement behaviours including comment automation, engagement pods, and unauthorised third-party tools.
Source: LinkedIn, How LinkedIn Is Improving the Feed (2026)
LinkedIn actively reduces repetitive low-substance posts and engagement bait such as 'comment to agree' prompts or videos that do not match their text.
Source: LinkedIn, How LinkedIn Is Improving the Feed (2026)
LinkedIn uses your profile data (industry, skills, experience, geography), your engagement history (reads, likes, comments, dwell time), and broader popularity signals showing that many similar professionals found the content valuable.
Yes. LinkedIn engineering published research showing that dwell time — how long you pause on a post without clicking — is a measured engagement signal. Long dwells alongside likes, comments, and shares are embedded into the ranking model's input sequences.
Yes. LinkedIn's official newsroom states that systems are being improved to reduce repetitive, low-substance posts and engagement bait, such as 'comment to agree' prompts or videos that do not match their text.
No. LinkedIn explicitly states it takes action to stop behaviours that promote inauthentic engagement, including comment automation, engagement pods, and unauthorised third-party tools.
LinkedIn is testing an Interest Picker during sign-up so new members can tell it directly what they care about. For existing members the system learns from their profile and engagement patterns over time.