Two years ago, I conducted a fascinating research project at Spotify as part of my Master’s Thesis. I learned several useful ML techniques, which I believe any Data Scientist should have in their toolkit. And today, I’m here to walk you through one of them.
During that time, I spent 6 months trying to build a prediction model and then deciphering its inner workings. My goal was to understand what made users satisfied with their music experience.
It wasn’t so much about predicting whether a user was happy (or not), but rather understanding the underlying factors that contributed to their happiness (or lack thereof).
Sounds exciting, right? It was! I loved every bit of it because I learned so much about how ML can be applied in the context of music and user behavior.
(If you’re interested in the applications of ML in the music industry, then I highly recommend checking out this interesting research led by Spotify’s top experts. It’s a must-read!)
Machine Learning & Behavioral Psychology in Tech
Image by Author (Midjourney)
In tech, research projects like mine are very common because a lot of the work revolves around delivering the best personalized experience for users/customers.