Show HN: Lagukan, a highly personalized music service

I'm planning later to make it adapt to preferences changing over time and also contextual factors like working out. There's a lot of good research on context-based music recommendation.

As time goes on I am planning to integrate things like this into Lagukan. I think a major problem with mainstream recommenders is that they mix together the two problems of 1) deciding when and how often to play songs the user already knows, 2) recommending entirely new songs. Lagukan is an ensemble recommender, i.e. it combines different algorithms. The core algorithm I've made is focused entirely on problem #1, but the plan is to incorporate lots of different approaches (e.g. algorithms based on compositional elements) for problem #2, and then have Lagukan be able to adapt to what works for individual people.

Most recommendations are either too broad (Louis Amstrong and Tony Bennett is not the same kind of jazz as Miles Davis or Weather Report) or too conservative (Youtube Music Your Mixtape is basically repeating same songs over and over never venturing out). And some seem more like paid adverts then recommendations.

So please grow and give me good recommendations. And I will pay for it.

The current app was optimized for development time, just so I could get a prototype working at least for my own use as quickly as possible.

Pandora’s concept of a music genome is quite fascinating too. For whatever reason the repeat rate gets too high though, even if you seed it with multiple songs.

Licensing within the music industry is the #1 enemy of getting good music cue suggestions across all music artists though, as many streaming providers can't just access any song they want to, because the agreements to play on each service are so limited and segmented.

I think they mainly want to prevent people from competing with them; I'm not sure if they care too much about charging you money for it (I guess they probably would if you were making a lot of money).

Also, thanks for your work on Mopidy!

edit: so after a quick search for "what is smoothing in machine learning", I think I'm doing something similar, though basic, already. Basically each value in the matrix is a tuple of (completions + j, skips + k), where j and k are constants. e.g. "In listening sessions where A was completed, B was completed 3 times and skipped 4 times." But if j and k are 2, then the effective values would be (5, 6). I got this idea from my bayesian textbook.

In addition, Spotify's algorithms have the significant constraint of needing to optimize profit, and not all tracks they have are worth the same amount (that's what someone told me, anyway).

Also, sometimes algorithms rely too much on explicit feedback like thumbs up/down rather than implicit feedback (like skips). Deezer's "Flow" feature is an example. The way you use it is basically the same as for Lagukan, but from reading reddit posts about it, it apparently just doesn't work very well. There was even a comment from a Deezer employee saying that it won't respond to skips; you have to use thumbs up/down to make it adapt.

Implicit feedback can be more messy to work with, but it's much more plentiful (and useful, if you use it correctly).

Also, if you sign up for an auth token on this page (https://lagukan.com/docs/), I'll send you periodic updates as the project progresses.