If you’re listening to music right now, chances are you didn’t choose what to put on—you outsourced it to an algorithm. Such is the popularity of recommendation systems that we’ve come to rely on them to serve us what we want without us even having to ask, with music streaming services such as Spotify, Pandora, and Deezer all using personalized systems to suggest playlists or tracks tailored to the user.
Generally, these systems are very good. The problem, for some, is that they’re perhaps really too good. They’ve figured out your taste, know exactly what you listen to, and recommend more of the same until you’re stuck in an endless pit of ABBA recordings (just me?). But what if you want to break out of your usual routine and try something new? Can you train or trick the algorithm into suggesting a more diverse range?
“That is tricky,” says Peter Knees, assistant professor at TU Wien. “Probably you have to steer it very directly into the direction that you already know you might be interested in.”
The problem only gets worse the more you rely on automated recommendations. “When you keep listening to the recommendations that are being made, you end up in that feedback loop, because you provide further evidence that this is the music you want to listen to, because you’re listening to it,” Knees says. This provides positive reinforcement to the system, incentivizing it to keep making similar suggestions. To break out of that bubble, you’re going to need to quite explicitly listen to something different.
Companies such as Spotify are secretive about how their recommendation systems work (and Spotify declined to comment on the specifics of its algorithm for this article), but Knees says we can assume most are heavily based on collaborative filtering, which makes predictions of what you might like based on the likes of other people who have similar listening habits to you. You may think that your music taste is something very personal, but it’s likely not unique. A collaborative filtering system can build a picture of taste clusters—artists or tracks that appeal to the same group of people. Really, Knees says, this isn’t all that different to what we did before streaming services, when you might ask someone who liked some of the same bands as you for more recommendations. “This is just an algorithmically supported continuation of this idea,” he says.
The problem occurs when you want to get away from your usual genre, era, or general taste and find something new. The system is not designed for this, so you’re going to have to put in some effort. “Frankly, the best solution would be to create a new account and really train it on something very dissimilar,” says Markus Schedl, a professor at Johannes Kepler University Linz.
Failing that, you need to actively seek out something new. You could seek out a new genre or use a tool outside of your main streaming service to find suggestions of artists or tracks and then search for them. Schedl suggests finding something you don’t listen to as much and starting a “radio” playlist—a feature in Spotify that creates a playlist based on a selected song. (These may, however, also be influenced by your broader listening habits.)
Knees suggests waiting for new releases or regularly listening to the most popular tracks. “There’s a chance that the next thing that comes up is going to be your thing,” he says. But getting away from the mainstream is harder. You’ll find that even if you actively search for a new genre, you’ll likely be nudged toward more popular artists and tracks. This makes sense—if lots of people like something, it’s more likely you will too—but can make it hard to unearth hidden gems.