An internal algorithm displays music

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Not based on user preference but on the similarity of the objects themselves. For example users usually listen to songs and. If a person starts liking the song then he is invited to listen to the song. Recently the service directly demonstrates its algorithm like this.  compatibility from other users and selects playlists based on matching tastes. In addition, you can also find out what kind of music people listen to and how.

Your music taste may match. The essence of this principle of content-based last database advertising is to create for each user an individual advertising virtual portfolio taking into account the characteristics of its elements (style, year, author, etc.). Such ad packages are created based on the user's preferences or direct query. For example a person listens to certain artists of punk rock and metal which means similar styles and authors will be recommended to him.



We usually buy a TV every few years. If the system recommends TVs based on other users' preferences then there are two risks that the matching may be incorrect and only recommend best-selling products. Therefore, the internal algorithm attempts to collect additional knowledge about the products and users involved in price filtering, interest dimensions, colors, brands, etc. There are several types of hybrid system recommendations.