4 ms·
Ha, I made something very similar with the goal of mesmerizing passersby at a conference where we had a booth: http://www.a1k0n.net/spotify/artist-viz/ http://w
by a1k0n 13y ago
Ha, I made something very similar with the goal of mesmerizing passersby at a conference where we had a booth: http://www.a1k0n.net/spotify/artist-viz/ http://www.a1k0n.net/spotify/artist-viz/
Used t-SNE on a vector clustering which came from LDA.
- dhammack 13y agoNice! I assume LDA was run on the lyrics?
- a1k0n 13y agoNot at all... "documents" were the user's play history and "words" were artists. So if you played one artist twice and another artist once it'd be like a document that says "artist1 artist1 artist2". The assumption is that document topics are analogous to music genres, and each artist creates music within a small set of genres each user prefers music within a small set of genres.
- dhammack 13y agoAh, that explains why the groupings are so good. That's cool, I hadn't thought about topic models outside of NLP before.