ravimody

> then we could name features

This is much harder and more time-consuming than it sounds, especially when you go from a small(ish) number of features to more general knowledge. Worse, it doesn't scale to arbitrary domains - you'll always need a human there to give meaning to the models and effectively train them.

Reinforcement learning is designed to get around this by letting an agent "learn" meaning on its own by interacting with the world and getting feedback from its current state and actions. https://en.wikipedia.org/wiki/Reinforcement_learning

HN link for part 1: https://news.ycombinator.com/item?id=12269568

I'm similar - I have an eclectic taste in music that is often not served well by static, long-term algorithms that place me into a "well" of the same sounding music. It's a problem I think about a lot.

Matrix factorization can deal with this a bit by using the high dimensional space to place your tastes into an area that reflects many different styles at the same time. In part 2 of the blog post we're going to talk about how we're modeling the acoustic qualities of music, which can find common patterns from completely different genres (for example, you may like soothing music with female vocals in both jazz and indie rock). In part 3 we'll talk a bit about how we can combine recent signals (like thumbs) to take into account your current mood, which I find helps pinpoint interesting music to surface right now.

Agreed, music map is a great interactive way to discover music. Last year one of our internal hack week teams built something like that using our vector space model. What was cool is you could switch between the different types from the UI, e.g. Nirvana -> Red Hot Chili Peppers -> DC101 Alternative Rock. You could also start stations from the UI - it was really fun discovering music with it!