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Statistics say  of income. McKinsey. More than half prefer algorithms over humans when they think decision-making is faster cheaper and more accurate  worse than humans Exposes far-right extremist activism and conspiracy theories about 2019 election results. As of this year alone recommendation engines search 100 million features per second and make 10 thousand model prictions. The most obvious way for bad reviews but only to stop bad recommendation questionnaires. Algorithms technical types Recommender systems can be very different from each other and use different methods data. Before analyzing a recommender.

In video views come from referrals

System for a single social network we consider Turkey B2B List the type of technology when creating the algorithm. before their interests will overlap in the future. The user-bas scheme is simple two Users have similar preferences for both music and artists. If a user likes a song that he hasn’t heard yet it’s likely that the listener will like it. In principle bas on statistical data about user preferences. A similar principle exists for good item-bas filtering. In In this case the principle is not bas on user preferences but on the similarity of the objects themselves. For example users usually listen to songs an.

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If users have similar interests

If a person starts to like songs then he is America Email invit to listen to songs.  This algorithm shows music compatibility with other users and selects playlists bas on matching preferences. In addition you can also find out what types of music celebrities listen to and how well your music preferences match. This principle The essence is that for each user an individual virtual portfolio is creat taking into account the characteristics of the elements style year author etc.. Such combinations are creat bas on the user’s preferences or directly ask. For example a person listens to Certain artists are punk rock and metal which means that similar styles and authors are recommend.

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