The principle of operation of recommender systems and their impact on people using YouTube as an example
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This article analyzes YouTube's recommendation system and explores its architecture, which is based on a two-stage machine learning model, taking into account the implementation of self-learning methods, and its evolution in 2025. The article focuses on identifying and systematizing the risks associated with content personalization, such as the formation of "information bubbles," the impact on user behavior, and the promotion of radical content. Recommendations for mitigating the identified negative effects on users and developers are presented, based on current scientific publications and sta...
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