#DearAlgorithms: introduction
This is the first part of an upcoming one month research project where I’m going to explore the filter bubble applied to the music industry.
Last week my friend Pablo sent me an email about his latest project: University of Error, a platform/collective/thinking group where the error is considered an important part of the learning process and life itself.
When Pablo asked me if I wanted to be part of it, writing about what error means to me, I immediately started telling him about the filter bubble and the recommendations algorithm used by music streaming services which makes the discovery of new music rather frustrating than helpful.
After a couple of emails back and forth, he suggested me to investigate the topic and come with conclusions for University of Error. And I thought that was an awesome idea.
The filter bubble and music
Since I read the book “The Filter Bubble” I’ve been a bit obsessed about this topic. The book maintains the theory that companies like Google are making our vision very narrow by personalising the internet according to our taste and searches. Basically they are creating flat and simple people to make it easier to categorise us, put us in a box and let marketers target us with products that we will for sure like to buy.
Here I want to focus on how the music streaming services are doing the same, and how their recommendations are affecting the way we consume music.
A simpler listener
My problem is, when I use Spotify, Rdio or Apple Music (I tried them all) they are incapable of giving me less obvious music recommendations. An example: when I tell them I like Arctic Monkeys, they suggest me to listen to Tame Impala and Two Door Cinema Club, which of course, I like. It’s obvious. But, how does that help me to be a smarter and more cultivated listener?
What this platforms are saying is, if you like indie, you will only like indie. And that’s not true, at least not for me.
The experiment
My intention is simple: I’m going to open a new account on four different services (Apple Music, Spotify, Deezer and Rdio) and for a month (yep, what a trial normally lasts on every streaming platform) I’ll be taking notes on how their recommendations evolve.
I’ll start exactly the same on every platform. I’ll tell it I like indie (because I know this genre and I can distinguish risky recommendations from obvious ones), that my favourite artist is Arctic Monkeys and I’ll see how far I can go from there.
My final goal is to create a comparison on how these different services are creating (or not) smarter listeners and helping break the filter bubble in music. During this month I will also compile some tricks and small hacks on how to help making your music service more open to other genres.
All for a smarter and more diverse music culture.
Follow the progress of this research here on Medium and on Twitter by the hashtag #DearAlgorithms and @alemasferrer.
Please share any personal thoughts, experiences or comments.
Thanks @angelablumen for the editing.
