Spotify’s Discover Weekly: How machine learning finds your new music

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This Monday — just like every Monday— over 100 million Spotify users found a fresh new playlist waiting for them. It’s a custom mixtape of 30 songs they’ve never listened to before but will probably love. It’s called Discover Weekly, and it’s pretty much magic.

I’m a huge fan of Spotify, and particularly Discover Weekly. Why? It makes me feel seen. It knows my musical tastes better than any person in my life ever has, and I am consistently delighted by how it satisfies me just right every week, with tracks I myself would never have found or known I would like.

For those of you who live under a musically soundproof rock, let me introduce you to my virtual best friend:

As it turns out, I’m not alone in my obsession with Discover Weekly—the user base went crazy for it, which has driven Spotify to completely rethink its focus, investing more resources into algorithm-based playlists.

Ever since Discover Weekly debuted in 2015, I’ve been dying to know how it worked (plus I’m a fangirl of the company, so sometimes I like to pretend I work there and research their products.) After three weeks of mad googling, I feel grateful to have finally gotten a glimpse behind the curtain.

So how does Spotify do such an amazing job of choosing those 30 songs for each person each week? Let’s zoom out for a second to look at how other music services have done music recommendations, and how Spotify’s doing it better.

A brief history of online music curation

Back in the 2000s, Songza kicked off the online music curation scene using manual curation to create playlists for users. “Manual curation” meant that some team of “music experts” or other curators would put together playlists by hand that they thought sounded good, and then listeners would just listen to their playlists. (Later, Beats Music would employ this same strategy.) Manual curation worked okay, but it was manual and simple, and therefore it couldn’t take into account the nuance of each listener’s individual music taste.

Like Songza, Pandora was also one of the original players in the music curation scene. It employed a slightly more advanced approach, instead manually tagging attributes of songs. This meant a group of people listened to music, chose a bunch of descriptive words for each track, and tagged the tracks with those words. Then, Pandora’s code could simply filter for certain tags to make playlists of similar-sounding music.

Read the source article at Hackernoon.