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p17 kaminskas. If You Like Radiohead, You Might Like This Article. Exploring music and how people listen to it through data. Spotify seeks more personalized playlists after Discover Weekly finds 40M users. Apple Music, Tidal and, until recently, Spotify, failed at music discovery because they stuck to a blog-style format.

Spotify seeks more personalized playlists after Discover Weekly finds 40M users

But Spotify has cracked the code with a Facebook News Feed-style personalized, frequently updated playlist called Discover Weekly. Spotify now says the playlists have seen 40 million unique users since launching last July, and 5 billion tracks streamed. That’s remarkable for a new feature, considering Spotify has 75 million active users total. Discover Weekly analyzes your listening history, combines it with what’s hot and new on Spotify, and updates each Monday with 30 new songs you’ll likely love. Song Dixon Pearce CMMR 2012.

Barrington Oda Lanckriet ISMIR09. Music Recommender Systems. Four Approaches to Music Recommendations: Pandora, Mufin, Lala, and eMusic. Thanks to MP3s and the Internet, we now have millions of songs readily available to us with the click of a button, but, paradoxically, this has often made it even harder to discover new music to listen to.

Four Approaches to Music Recommendations: Pandora, Mufin, Lala, and eMusic

Every online music store and every social network that focuses on online music, however, now features some kind of music recommendation system, and some services like Pandora or Slacker Radio are indeed nothing else but highly sophisticated music discovery engines. In this post, we will look at the different approaches behind some of the most popular music recommendation and discovery services. Currently, we are seeing four different approaches to giving music recommendations in the market place – though the lines between them are often fluid and some services mash them up in different ways. Spotify. Recomender Systems. Recommender Systems. Recommender systems, Part 1: Introduction to approaches and algorithms. Learn about the concepts that underlie web recommendation engines M.

Recommender systems, Part 1: Introduction to approaches and algorithms

JonesPublished on December 12, 2013.