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 embeddings of the two songs and find "close" songs to points along that line. This should give us a somewhat smooth transition - at least that's the hope.
I suspect some of it may be that we are using a euclidean line through the vector space, but using cosine distance for similarity. We're still trying to get the hang of using the vectors to build a smooth transition between songs.
We are also tuning our model and training variables, as well as pulling in more playlists, which should help (I hope).
We are essentially trying to draw a line between the embeddings of the two songs and find "close" songs to points along that line. This should give us a somewhat smooth transition - at least that's the hope.
I suspect some of it may be that we are using a euclidean line through the vector space, but using cosine distance for similarity. We're still trying to get the hang of using the vectors to build a smooth transition between songs.
We are also tuning our model and training variables, as well as pulling in more playlists, which should help (I hope).