Music recommendation is such a hard problem. There are all these seemingly obvious relationships you can map between bands to create a big graph that looks good but that almost never captures what goes on when a human with deep music knowledge recommends music. Often the best recommendations have no obvious relationships to the band you like.

I played around with this tool a bit and didn't find it any better then other more traditional music discovery tools, that is to say not very effective.

For example, I entered John Zorn and was recommended a bunch of John Zorn's bands. I entered The Residents and got The Pixies :/

I think its more effective to click around on Music Brainz and Wikipedia.

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cousin_it 7 hours ago | parent | next [-]

For me music discovery is a solved problem. Here's the algorithm:

1) Imagine the timeline of musical history. If you don't have a clear idea of it, Wikipedia is a good place to start.

2) Pick any genre/period you don't know well. (For example, medieval music, or swing-era jazz.)

3) Look up the main figures of that genre/period. (For example, Guillaume de Machaut, or Duke Ellington.) Wikipedia is good for this too.

4) Listen to a sample of their most well known pieces. YouTube is good for this.

5) Repeat. Go down rabbit holes when you like.

No fancy tools needed, just your mind and the internet. This will give you interesting music for many years, and improve your musical taste a lot too.

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deklesen 7 hours ago | parent | prev | next [-]

You seem knowledgeable about this.. Care to test my old project for music recommendation? I built it by looking at co-occurrence of artists in Spotify playlists, which gives me word2vec-style vectors, and then its just kNN.

No login needed, just enter some artist names and see what you get:

https://blog.jonas-klesen.de/artist2vec

	▲	hsur8192 5 hours ago \| parent [-]
		Very interesting, I've been working on a similar project (using word2vec to learn vectors using playlist data), but using songs instead of artists as the 'words'. The main bottleneck at this point is the volume of data - many songs I'm interested in only are only represented in a handful of playlists, and . Evaluation at any useful scale is also quite difficult. For somewhat obvious reasons, in our AI era Spotify has become quite skittish about letting third parties gain access to their data at scale...

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jzb 2 hours ago | parent | prev | next [-]

Nothing beats humans with great music tastes and deep knowledge. I’ve yet to find any form of recommendation engine that has surprised and delighted me the way humans have.

This tool might unearth something interesting, but I find it sus that it’s recommended the same artist (Adrianne Lenker) when I asked about Aimee Mann and Steven Jessie Bernstein.

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BonoboIO 6 hours ago | parent | prev | next [-]

Pandora solved this problem nearly 20 years ago an Spotify with all its money and engineers do such a bad job, it’s beyond absurd.

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BLKNSLVR 3 hours ago | parent | prev | next [-]

If you're into John Zorn and The Residents, you gotta check out Angine de Poitrine: https://www.youtube.com/watch?v=0Ssi-9wS1so

Microtonal polyrhythmic looping absolute madness. (you can hear some Primus and King Gizzard and the Lizard Wizard kinda sounds in there, if they also tickle your fancy)

Residents -> Pixies is certainly an odd recommendation. Having said that, where _can_ you go from The Residents? Daniel Johnston?

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tremarley 7 hours ago | parent | prev [-]

Spotify seems to have mastered music recommendation.

It would be great if somebody could reverse engineer their recommendation algo

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deklesen 7 hours ago | parent [-]

Actually, I find Spotify horrible for finding new music outside my bubble that i like. YouTube works much, MUCH better for me for this purpose.

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chrisweekly 7 hours ago | parent | next [-]

Interesting. Spotify works almost perfectly for my discovery needs. I just pick a track I know that fits my mood, then use the (3-dot menu) "Go to Radio" option, which leads to a playlist that usually includes tracks and/or artists new to me. It's been a reliable discovery mechanism for me for many years. Also, there's a new feature I first saw within the last week, a "non-personalized" option that increases the "new to me" ratio.

	▲	aworks 4 hours ago \| parent \| next [-]
		Hmm, just tried the non-personalized option for the first time. Is it a reflection on me that I may prefer it over "personalized"?
	▲	plaguuuuuu 5 hours ago \| parent \| prev [-]
		the "you might also like" for a given artist is usually the most generic related artists - for anything remotely related you'll get basically the same list which is the middle of the venn diagram of everyone who listens to them

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jmye 5 hours ago | parent | prev [-]

I always find this interesting… Spotify is phenomenal for me - about every third Monday Discovery playlist has two or three hits, which feels like a pretty solid ratio, at this point. YouTube has never suggested a single thing I cared for.

I wonder if it’s a curation thing? I’ve been with Spotify since the first day it was available, and rarely use YouTube. I haven’t had a good music ratio as good since newsgroups and (real) forums a decade ago, which were a different form of curation.