warpri81

5 ms·

1. 1.▲
   by warpri81 9y ago

   This is true. I wish we had their entire playlist library! This was trained on playlists we scraped from Spotify, so by definition pop music is going to be heavily represented. We are still playing with the training parameters and algorithm
2. 2.▲
   by warpri81 9y ago

   I suppose it is kind of like Pandora. We are actually mimicking word vectors (ala word2vec) but using playlists as the data set. So, in theory, it IS learning the context of songs to make some inferences about the songs themselves. I absolu
3. 3.▲
   by warpri81 9y ago

   Thanks for taking a look! We basically try to draw a line between the two songs you chose and find songs close to that line. It can produce some weird results, and it very seldom seems to be the smooth transition we were shooting for. We ar
4. 4.▲
   by warpri81 9y ago

   We use a shallow neural network to learn about songs from playlists we scraped from Spotify, but to actually build the playlist we use some straightforward vector math. Rarer songs/genres do tend to be all over the map, and certain sup
5. 5.▲
   by warpri81 9y ago

   You are probably correct about the vector math! We learn low level embeddings from playlists we scraped from Spotify (with little discretion) and are still working on the algorithm. We are essentially trying to draw a line between the embed
6. 6.▲
   by warpri81 9y ago

   Thanks for checking it out! We used thousands of Spotify playlists to train on, but obviously more popular genres are going to be heavily represented. For rarer songs, it may not pick up on the association between two songs. We are still tr
7. 7.▲
   Show HN: Momixa – Custom playlist mixes using machine learning(momixa.com)

   38 points by warpri81 9y ago|17 comments