![]() ![]() The users who have similar habits are more likely to have similar taste profiles. Filtering Collaboration: This uses information about other listeners which contributes to the algorithm’s understanding of the users’ listening habits.Generally, Spotify relies on three recommendation models: The listening platform of Spotify is designed to include built-in ego-boosters for its listeners. This is how Spotify attaches its users to itself. Haley Weiss mentions in The Atlantic, “ seeing top songs on Spotify Wrapped is like seeing an old best friend that you lost touch with”. Not only is their music data neatly packaged, but also is very well presented. That is the kind of emotional validation that makes users feel personable. ![]() For them, it could be like sharing the songs that they like or it could be that Spotify acknowledges favourite artists. In simpler words, Spotify provides its music data back to its users. It makes them feel like it’s a personality test. However, the presentation of the data is something that excites people. In addition to that, they mention if they are among the bold, non-mainstream listeners. ![]() This report, mentions that those users are in the top 1% of a band’s most loyal followers. When the year-end arrives, users get a report. There is no way that Spotify Wrapped involves the use of basic analytics. ![]()
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