There’s a lot of music recommendation systems out there that give pretty obvious and predictable suggestions. They suggest the Beatles if you like the Stones, and Aphex Twin if you’re a Squarepusher fan. I find them pretty dull and uninspiring.
Some of the best music recommendations I’ve had have been very wild jumps, to music with similar qualities in very different genres. A friend once recommended John Coltrane when I said I liked Squarepusher. Years later another guy suggested I listen to Steve Reich when I said I liked Coltrane. I wanted to try and capture some of what was going on in their recommendations.
Akin To is my attempt at a more imaginative and literary kind of music discovery, letting you explore and compare music through the adjectives in album reviews. Try searching for “cinematic”, “enigmatic” or “space-age” music and you’ll see albums described with those words. Look up an album or artist you like to see other music that’s described similarly.
I’ll share a few buzzwords and bullet points about the creative and technical challenges of making it:
- I used Python’s Natural Language Processing Toolkit to detect adjectives in the reviews.
- The similarity between two albums is based on the number of adjectives their reviews have in common, and how unusual those adjectives are.
- There’s a graph database underneath it, implemented with MySQL. Mmm, graphs.
It’s an experimental project and I’d love to hear what you think about it, please leave a comment! And if you’d like to show your thanks and support further development, why not sponsor your favourite adjective?
Huge thanks to Pitchfork who’s reviews fuel Akin To’s engine. They have all the best adjectives, and their writing is perfect material for a project like this.
Thanks to everyone who helped me make it. Jamie Matthews inspired me to learn Django a few years ago and I’ve never looked back, and Tomek Kopczuk helped me with a particularly tricky database issue. Clare Sutcliffe did all of the beautiful mobile and desktop UX and design that you see on the site. The concept, algorithms, full stack development and everything else, especially the mistakes, are my own.