I run a self-learning music discovery engine called Gnoosic:
I can confirm that when you suggest a random band to a random user, they will dislike it with over 90% probability.
I'd be interested to hear how well Gnoosic works for your musical taste.
I can confirm that when you suggest a random band to a random user, they will dislike it with over 90% probability.
I'd be interested to hear how well Gnoosic works for your musical taste.
My favorite example is Seven Sirens And a Silver Tear from Sirenia, a Norwegian gothic metal band. There's no metal in that. It took me a long long time before I learned this but the track is a direct descendant of the Midlight Sonata. And I was hunting for similar songs and I now keep a playlist of them -- but if you started from Sirenia you would never find any of them.
The good news is that in the digital era you no longer need to fork over cash for an entire album or even an EP when you only care about one of the songs - which leaves more money available to buy music from other artists. I often wonder if in the long run it still balances out for artists, since the songs one person likes probably aren't the same as the songs another person likes, especially in niche genres.
Do you think online NNMF collaborative filtering with Spotify bands with fewer than 100,000 monthly listeners is the answer? If you had infinite resources, what would you do?
I wish I could give it more bands, and see the distance (I imagine it computes one?) between each band I provided and the ones it suggests.