Once the content retrieval is finished, the ecosystem owner can look at the results and curate the information.
In order to assess the relevance of the articles, the top 100 most relevant articles are presented in a ranked list. The order of this ranking is determined by the DataScouts algorithms that take the content of the concept search into account but also learn from the user feedback.
The summary of the article gives an overview of the title, its content, the keywords that the article contains, the date and a black star, which indicates that the user has marked the article as relevant. When the user has marked the article as relevant, the DataScouts algorithms will use this user feedback to recalculate the relevance of the other articles and bring similar articles more to the top of the list automatically. This is one example of how user-generated feedback is taken into account by machine learning. When the user opens the article, a new page opens that provides full detail about the article. additional information about the content of the article.
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