Quartz :

The connection between data from 2 billion playlists and your personal taste profile is made by Spotify’s algorithms. This is the secret sauce, and it gets complicated quickly.

Spotify engineers shared many of the technical details in a presentation earlier this year. Their approaches include collaborative filtering, most commonly seen in Amazon’s “customers who bought this item also bought…” feature, and natural language processing, which is how Echo Nest understands music blogs and the titles of playlists. The company uses the open-source software Kafka to manage the data in real-time.

 But you don’t need to understand any of that. This is how Ogle described the process to me in layman’s terms:

“On one side, we’ve built a model of all the music we know about, that is powered by all the curatorial actions of people on Spotify adding to playlists. On the other side, we have our impression of what your music taste is. Every Monday morning, we take these two things, do a little magic filtering, and try to find things that other users have been playlisting around the music you’ve been jamming on, but that we think are either brand new to you or relatively new.”