In fact, my "starred" playlist radio is currently stuck on some kind of 80s metal thing. I have no idea how it got that way, but it refuses to play anything but metal, when that's not really what I like. If I make a copy of my starred playlist and make a radio from the new playlist, it reflects the actual songs at hand..
There's also the matter of unique bands that while fitting into a very wide genre, don't really fit into any widely known sub-genre. Meaning radio for the band is almost useless in many cases. There is the "related bands" bit which helps it a bit, but this only works with popular bands and still is very mismatched often times
Curious if you think the low-level features learned from the vector_exp latent factors are different from, say, unsupervised learning with sparse autoencoders? For example, are there phonemes associated with Chinese pop or Spanish rap that are learned at a low level, that the network might not learn with "unlabeled" data?
With a purely unsupervised approach, you are basically wasting capacity on modeling aspects of the data that are relevant for reconstructing the input, but not for solving the task at hand. For example, the model doesn't have to care about precise pitches and timing, because those are not relevant for recommendation (and latent factor prediction) anyway. That means no model capacity is wasted on these things. With a fairly complex task such as this one, I think that probably makes a big difference.
I'm not interesting in scrobbling to another service, etc. Seems like this should be a well documented and used service from the Spotify API, but as far as I know, it does not exist.
I agree listing history is more usefull
With the intriguing exception of the Global Temporal Pooling layer, this matches up with a lot of my ideas for music analysis. Nice work!
As a sidenote, if it weren't for the L2-pooling in the global temporal pooling layer, the network would be completely piecewise linear from input to output :)
There is a lot of knowledge about good time-frequency representations for music analysis among the people who were formerly at The Echno Nest (which was acquired by Spotify a few months ago), so it would definitely be a good idea to apply their knowledge to this and come up with a better input representation. I only have a few weeks left though, so it doesn't have a high priority at the moment :)