A visualization of every song on every Spotify Release Radar playlist over the course of a year. Filled in squares equal songs that were listened to.

I Decoded the Spotify Recommendation Algorithm. Here’s What I Found.

A print version of this analysis can be found here.

Man vs Media vs Machine

I assumed that the prominence of recommendations built into Spotify would dominate my discovery. But to really see if that was true, I needed to dig into the data. Through the course of the year, I tracked ever single music recommendation I received — from friends and colleagues to Spotify algorithms to social media. Side note: In 2011, my data collection was all manual, with pen and a notebook. In 2017, only 8% of the data I collected was done manually. That’s the byproduct of music’s migration to software platforms.

Dates align with each side of the triangle, counterclockwise from corner to corner — January to December

This Machine Makes Playlists, Playlists Make Me Listen

After years of believing that I was an album listener, in 2015 I discovered that playlist listening had an equal presence in my listening behavior. We are squarely in the era now of the playlist, thanks in large part to Spotify. I wanted to see if the ease of listening to recommendation playlists somehow gave them an advantage. Those songs were only a click away whereas other recommendations required a little more work.

Most recommendations come in at midnight (from playlists) or in the morning (from social media) and are then listened to throughout the day.

Product vs Magic

Many tout the machine learning voodoo at the core of the Spotify recommendation algorithm. The product rules appear to be just as important. They take the magic and make it useful. I was able to to infer a few product rules from analyzing the Release Radar playlist:

  • Keep the song on the playlist for up to 4 weeks if it hasn’t been listened to.
  • Favor artists that I have listened to before.
  • Use remixes and Spotify live recordings when new music runs thin.
  • Try all types of songs to keep the playlist fresh and exciting.
There’s really no strong pattern in the song attributes of machine-based recommendations. They will try anything.

The Missing Piece

Algorithmic recommendations in Spotify are easy to get to and pretty much in line with my current tastes. As I looked deeper into the data, I saw that they didn’t do much to get me out of my comfort zone. I realize that if the playlists were full of artists I didn’t know or didn’t normally listen to, I would probably ignore them. So it takes more than just exposure to drive discovery.

The Next Seven Years

Music discovery will continue to change. Adaptation is already happening. Historically influential sources of music discovery, like Pitchfork and other music blogs, have adapted to the era of playlists. They now have their own playlists that they update weekly. Evolution is beginning with the integration of Spotify in Instagram Stories. Music podcasts, the saturated music festival landscape, and smart speakers are providing additional avenues for finding new music.

Music, Data, and #musicdata | www.ericboam.com