LinkedIn's Collaborative Articles feature has reached a milestone of 10 million pages of professional content in one year. The Collaborative Articles project has seen a significant increase in weekly readership, increasing by over 270% since September 2023. How they reach these milestones and plan to achieve even more, learn valuable lessons for creating his SEO strategy that combines AI and human expertise. Offers. .
Why collaborative articles work
The intuition underlying the Collaborative Articles project is that people turn to the Internet to understand subject topics, but what is on the Internet is not necessarily the best information from actual subject matter experts. That means no.
Typically, people search on Google and sometimes go to sites like Reddit to read posts, but they don't know if the information is from experts in the field or from the biggest names on social media. There is no guarantee that it came from the person with the mouth. How can someone who is not a subject matter expert determine whether a stranger's post is a trustworthy expert?
The solution to this problem was to leverage experts on LinkedIn to create articles on topics they specialize in. When a page ranks on Google, this benefits subject matter experts and motivates them to write more content.
How LinkedIn designed 10 million pages of specialized content
LinkedIn identifies subject matter experts and contacts them to write essays on the topic. Essay topics are generated by an AI “conversation starter” tool developed by the LinkedIn editorial team. These conversation topics are matched with subject matter experts identified by LinkedIn's skills graph.
The LinkedIn Skills Graph maps LinkedIn members to subject matter expertise through a framework called Structured Skills. Structured Skills uses machine learning models and natural language processing to identify relevant skills beyond what members themselves are aware of.
Mapping uses the skills found in member profiles, job descriptions, and other textual data on the platform as a starting point, and from there uses AI, machine learning, and natural language processing to identify the skills a member may have. Expand your expertise in additional subjects.
The Skills Graph documentation explains:
“If a member knows something about artificial neural networks, that means they know something about deep learning, which means they know something about machine learning.
…Our machine learning and artificial intelligence examine vast amounts of data and suggest new skills and relationships between them.
…Combined with natural language processing, we reliably extract skills from different types of text, ensuring high coverage and precision when mapping skills to members…”
Experience, expertise, authority, trustworthiness
The basic strategy of LinkedIn's Collaborative Articles Project is genius in that it generates millions of pages of high-quality content from subject matter experts on millions of topics. That may be why LinkedIn pages are becoming more and more prominent in Google searches.
LinkedIn is currently improving the Collaborative Articles project with features aimed at further improving the quality of your pages.
- The way we ask questions has evolved:
LinkedIn now presents subject matter experts with scenarios to which they can respond with essays that address real-world topics and questions. - New useless button:
Added a button that allows readers to send feedback to LinkedIn that a particular essay is not useful. Building the thumbs down button around the paradigm that LinkedIn is helpful is very interesting from an SEO perspective. - Improved topic matching algorithm
How LinkedIn matches users to topics using something called Embedded-Based Search to Improve Matching, which was created to address feedback from members about the quality of topic-to-member matching. has been improved.
LinkedIn explains:
“Based on feedback from our members through our rating mechanism, we focused on the ability to match articles with member experts. One of the new methods we use is embedding-based retrieval (EBR) This method generates embeddings of both members and articles in the same semantic space and uses an approximate nearest neighbor search within that space to generate the article that best matches the contributor.
Important points about SEO
The LinkedIn collaborative article project is one of the best strategic content creation projects I've seen in a while. What makes this book not only genius but revolutionary is that it uses AI and machine learning technology alongside human expertise to create professional, useful content that readers will enjoy and trust.
LinkedIn currently uses user interaction signals to improve the quality of subject matter experts invited to write articles and to identify articles that do not meet the needs of our users.
The benefit of writing articles is that every time an article ranks on Google, a quality subject matter expert gets promoted. This gives you the opportunity to promote your services and products and prove your skills and expertise to clients or anyone looking for their next job. and authority.
Read LinkedIn's announcement about the project's first anniversary.
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