Matrixnet is a machine learning technology developed by Yandex that is used to build the formula that determines the ranking of search results. It is a complex algorithm based on gradient boosting methods, which create multi-layer models that analyze a huge number of factors when assessing the relevance of pages.
Matrixnet’s main strength is its ability to detect complex dependencies between various SEO signals – such as user behavior, content quality, external links and other parameters – without the need for manual tuning. This allows Yandex to arrange results in a way that more accurately reflects the actual usefulness of the content for the user.
Matrixnet has a high resistance to overfitting, which means that the algorithm does not over-adapt to specific examples from the training data, but retains its ability to make accurate predictions in new cases. Thanks to this technology, Yandex is able to maintain more precise and dynamic rankings that respond to changing behavioral signals and search trends.
Matrixnet
