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YouTube's recommendation algorithm relies on over 80 billion daily pieces of information

These signals include which videos a user has watched, searched for, and liked, as well as channel subscriptions and feedback selections, such as videos marked "not interested." Each user's viewing habits are compared with those of other users with similar habits, and this information is used to suggest additional content.

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