SandS Media is a European leader in the developer community with sites like devm.io and Entwickler.de. They educate full stack developers with know-how on topics like: DevOps, JavaScript, Rust, Security, Machine learning and many more. Through their partnerships with companies and their conferences like: MLCon, DevOpsCon, APIconference, Jax and International JavaScript Conference, they offer a wide array of education possibilities for their community.
Over the last 25 years, SandS Media has built up an impressive directory of 12,000+ articles for developers written by experts. These website articles are the lifeblood of the company. It is what brings their community the value they seek. However, with that amount of content available, it is not always easy for their readers to navigate through it and find the most valuable articles for their specific needs.
To better tailor the experience of the reader, SandS Media has been building a recommender system. Each article is categorized and rated on a scale from 0 to 10 on about 60 different topics by their editors.
Throughout the exercise of categorization and rating, they encountered 3 problems: (i) the amount of work for editors to categorize and rate the 12,000 articles (ii) ensuring consistency as not each editor would make the exact same judgement, and (iii) to make the categorization and rating available in structured way so it could be consumed by their recommender system via REST-API each time a new article was published.
To solve the above, Structize AI developed a “Struct” based on Large Language Models which resulted in a significantly higher consistency, less work to be performed and in a consistent output format. Now every article is being categorized by the same standards and fully automated each time that an article is published. This adds excellent value to their community as they get more personalized articles suggested based on their personal preferences.
Dr. Pouya Kamali-Loibl, CEO SandS Media says: “An editor spends 5 to 10 minutes to categorize an article. If we had to label the 12,000 articles, this would have resulted in a tremendous amount of work. We know from our past efforts that consistency was an issue too. We have been researching a solution for a long time and Structize AI has solved this problem for us in just a couple of days. I would absolutely recommend working with their team.
Are you interested in website articles categorization for your business? Get started with Structize AI here: book an initial intake for EUR 99.-
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