I had always assumed that recommendation algorithms didn't work on genres, but instead did a lookup based on something like:

"You like X" "Person Y also likes X" "Person Y also likes Z" "I should recommended Z to you" (proportional to the number of people there are who liked X and Z, compared to other options)

If this is the case (and perhaps I'm wrong), it's nothing to do with the algorithm not understanding the subtleties of the genre, but it's actually just that on average those subtleties disappear because they are unique to you. (Forgive me, I don't know your genres at all, but), I would guess that on average people who like Black Sabbath really do typically like Slipknot as well, and that is why the algorithm recommends them to you.

It's not that the algo is any worse than human recommendations, it's just that it's an average of all humans likes, which will never fit your exact personal unique preferences because they aren't you.

The term for this is https://en.wikipedia.org/wiki/Collaborative_filtering

> It's not that the algo is any worse than human recommendations, it's just that it's an average of all humans likes, which will never fit your exact personal unique preferences because they aren't you.

But that is a difference though, because humans possess empathy: algorithms don't. Step away from music for a second and consider movies: if all your algorithm does is recommend movies because Person X likes Movies 1, 2, and 3, it will take many layers of metadata tagging before the algorithm figures out that it shouldn't recommend Movie 2 to Person Y (even if Movie 2 is a star-studded, Oscar-winning classic), while a human being could probably spend 30 seconds looking at Person Y's viewing history and realize, "Ah, this person has very few action movies and zero horror movies in their viewing history; they probably don't like violence. I would not recommend they watch Movie 2."

That’s not really true at all. Graph algorithms can look at the movies you like and find other people that have uncommonly common movie likes as well and find a movie to recommend based on this. You avoid just recommending movies that are generally popular across all users.

For instance, if someone likes “”Caddy Shack” it isn’t too difficult to recommend “Space Balls” for example and not “Schindlers List”, even though everyone likes that movie. But “Space Balls” would stick out amongst the population of people that like “Caddy Shack” vs the population as a whole.

No meta data is required. Just preferences over a large set of users.

That's the way music-map.com works, and it is absolutely fantastic.

But no, that is NOT the way these other services recommend things.

Spotify, for example, recommends music that it thinks is related, but not necessarily liked by both groups of people.

For example, a person who likes Black Sabbath, and really technical instrumental metal, probably doesn't like the comparatively simpler slipknot. While some people who like slipknot may like black sabbath, but even less will be aware of really technically advanced instrumental metal.

music-map.com will take that into account. When you say "my three favorite bands are", you're not going to see slipknot show up in the result list, because people who mentioned those technical bands tended not to like slipknot.

But if you go to spotify and listen to sabbath, you'll see slipknot recommendations, because there is overlap, right?

I just went over to music-map right now, and sure enough, black sabbath and slipknot don't overlap, even when you do searches of the bands individually, let alone if you enter multiple bands.

So I put in Silver Jews, and expected to get probably the #1 result "Purple Mountains" which is also David Berman, and then pavement and some Malkmus as recommends. So I don't know how well that works.

Coheed and Cambria seemed fairly good. I was expecting Thursday as a top result and got it.

I put in "Mom Jeans" and a totally very different sounding band but on the same label who tour together and got "Just Friends". That's an unexpectedly nice connection.

Yeah, Last.fm is really good for playing a playlist from a single scene. It's not necessarily a single genre, just bands who tour together, or guest on each other's tracks.

They used to go pretty deep too, like bringing in bands from 20 years before that influenced the current one.

It's not necessarily the greatest for bands that are selling out stadiums as there's too much interference from Clear Channel, but it's great for small to midsize scenes which tend to have shows from 100-1000, since those tend to be more defined.

You have to now wonder how much of last.fm has since become tainted by faulty recommendations coming via Spotify and co though.

did you catch a Pavement reunion show?

Spotify recommends me tracks that I have already added to my playlists. It knows me really well.

I dunno, if I was writing the Spotify recommendation engine and wanted to trick people into thinking it was good, I'd do the same thing.

> For example, a person who likes Black Sabbath, and really technical instrumental metal, probably doesn't like the comparatively simpler slipknot

That's just thinly disguised metal snobbery. Almost all metalheads adore Black Sabbath, because they wrote fantastic songs. Sabbath's music caught on because it's so accessible. They were certainly good musicians, but by no means anywhere near the top of the Technical Sophistication Food Chain.

I myself am a cretin, and thus have little patience for technically advanced music, but I'm happy to accommodate a vast world of music where everyone finds something they like, so I'm not going to denigrate anybody's favorite Super Technical Metal Band with twin guitar solos and the 75-piece drum kit - it's all good to somebody. But you've gotta be kidding if you think Sabbath is that kind of act.

> Almost all metalheads adore Black Sabbath

This implies that, if there are many different subgenres with little overlap in audience but they all agree on Black Sabbath (and little else), drawing conclusions from "listens to Black Sabbath" is doomed to fail. "Listens to Black Sabbath" simply isn't particularly specific, and those subgenre-heads are all about specifity. Same as that you can draw a lot more conclusions from "likes listening to Number Nine" than from "likes listening to Yesterday"

> That's just thinly disguised metal snobbery. Almost all metalheads adore Black Sabbath, because they wrote fantastic songs

I think you might have misunderstood the parent a bit. They're not saying Black Sabbath is really technical instrumental metal but that someone who listens to Black Sabbath AND ALSO likes really technical instrumental, is probably not looking to listen to Slipknot (though I suppose that could depend on the technical to Black Sabbath ratio).

The point is more that Spotify supposedly recommends without taking into account what people actually listen to. Spotify says "oh this person listens to Black Sabbath which is a popular metal band. They also listen to a lot of Blotted Science, which is also a metal band. They most likely will like Slipknot, another popular metal band" instead of "This person listens to Black Sabbath and Blotted Science. People who listen to both Black Sabbath and Blotted Science also listen to Nile, so this person will most likely like Nile"[0].

[0] Blotted Science and Nile are the only technicalish bands I know off the top of my head. If those bands don't work, try Blind Guardian and Stormkeep

Metal snobbery is a legitimate taste, though. It's the sort of thing a recommendation algorithm should consider.

The thing a human recommended can do is say “based on your listening history, I think you'd also like this band... but wait, they're a famous band. Surely you'd know about them already. So the fact that you don't listen to them suggests that you don't like them. Ok, I won't recommend them.” Computers don't yet make that logical leap -- they don't yet use what's missing from your listening history as additional data.

> It's not that the algo is any worse than human recommendations, it's just that it's an average of all humans likes, which will never fit your exact personal unique preferences because they aren't you.

Isn't that the definition of "worse"? "Never able to be tailored to your unique preferences" when a human could take that feedback in a single sentence and adjust? It's like the old "everyone wants to remake Excel since they realize 80% of users only use 20% of it... without realizing it's always a different 20%" saw...

I believe that you certainly could figure out a smarter approach, with enough careful thought, domain knowledge, and effort but... I haven't used any services that do it. Surface level similarities seems to be all you get, and I constantly get recommendations that a human would've stopped giving me years ago.

Maybe the domain of expert-level statisticians and experts-at-musical-knowledge-of-many-genres just don't overlap that much? It's hard to imagine you could do it truly well without content-level processing, not just user histories and connection graphs.

Just because no streaming service could afford a human to be an expert on every single user doesn't mean that the cheaper, more scalable algorithm isn't worse.

I mean, maybe it's not worse though. Maybe others would find what the other guy wants too narrow. There's a tension between only playing stuff you are 100% going to like and actually introducing you to stuff you haven't listened to.

> Maybe others would find what the other guy wants too narrow.

That's exactly what makes it worse, though. They currently have to choose more globally, it's not that personalized. in the human-curated world others would get different things. I know they don't like Slipknot, I know you do, I give you each different recommendations. Spotify's code hasn't shown the ability to learn "me" vs it's view of what the "average me" would like.

Can't you refine your results by telling it to stop recommending an artist?

IME, human recommedendations are (were) generally not "an average of all human likes". The parent's comment is spot on, IMHO. Granted I have an opinion that may be biased toward a pre-internet, pre-www world, where friends and I shared vinyl, tape and later CD and DAT collections. Most of the music I collected came from friends' collections1 rather than purchases or live recordings I made myself. Recommendations were important, but also important was that we usually perused what was in our friends' collections. If I found an item that looked interesting, I could ask what the owner thought about it. That might produce another "recommendation".

1. Those whose collections were much larger. They were not purchasing LPs, recording live concerts or collecting live recordings on the basis of recommendations, they were taking chances on artists that they may have heard about but with which they were not familiar. Obviously sometimes they acquired stuff that was less interesting than what they had hoped. But it is through mistakes that we learn. The www- and smartphone-era recommendation algorithms are designed with intentions that friends never had. Friends were not collecting data on internet users and/or selling online ads.

> an average of all humans likes, which will never fit your exact personal unique preferences

Well, it's not as if that's an unknown problem in machine learning. In all generative domains (and I suppose recommendations are a generative domain in a sense) there's a risk of "mode seeking", where the model produces bland/blurry/gray output in order to cover its bases, and not miss too badly. It's rarely what we want.

There's also the often quoted "Most of the volume of a high-dimensional orange is in the peel, and the average of two vectors is very unlikely" (Well, I like to quote it because it's fun to say. I forget where it originally came from.) In plain English, if you need more than two dimensions to characterize variation, then you need to use spherical interpolation to find a likely data point in between them, not regular interpolation.

It's surprising how many times this has been overlooked and rediscovered.

I've found most recommendation systems in general don't grasp the distinctions in genre that are in metal, or the stylistic differences. Most stuff is just tagged "METAL", when there is a huge difference between grind, funeral doom, or atmo-black. Even within a genre there can be differences it doesn't really capture, like tech-death: you might call Beyond Creation and Warp Chamber both tech-death, but in reality they are pretty far from each other even though they are both technical in a tech-deathy way.

It depends on how the recommendation system define its music genres.

Spotify uses audio spectral features to automatically delineate tracks into styles.

Last.fm and RateYourMusic (who added recommendations recently) rely on human taggers. So, they may give you better results genre-wise.

How do you find the https://everynoise.com/ categories to fare at this?

This is Spotify’s list of "genres".

To be fair (and more exact) Spotify data scientists detect patterns in the audio spectral features and use them to manually create/add new genres. The "collective wisdom" nature of last.fm and RYM will often give better results/phraseology.

You could say that Spotify could also be data mining these Music Social Media sites! My own research does not show this to be true (at a large scale anyway). But they should (and pay them for it)

I mean, I'm pretty sure it is not just audio spectral features used, though I can't find a citation either way. (I am sure because the language distinctions make clear that we are at least making heavy use of metadata if not actual social patterns in listening, but the latter is explicitly heavily used elsewhere on the site, so...)

Also, if we're being exact, it's the work of Glenn McDonald under the auspices the Echo Nest, which is now owned by Spotify, but it isn't the be-all-end-all of how Spotify models genres under the hood.

What I was curious about is how /u/beezlebroxxxxxx perceived it to do at the metal genres, simply because Glenn McDonald is himself a metal genre fan (https://www.furia.com/), so I'd've expected that to work decently well.

Yeah, music and other such automated recommendations are truly infuriating - I like to imagine it is an attempt to tweak people instead of helping them. This belief is reinforced when often these recommendations don't come with a 'never recommend again' or 'stop recommending altogether, please' option. I am grateful though that such 'helpful' behavior is often successful in helping me get off the service in question and not use it again.

Reminds me of the old facebook joke: We went to the same high school, we have over 100 common connections, you have recommended him to me 10 times already, and we are still not friends. Get a clue, Facebook!

> Human review and recommendation still beats algorithmic recommendation by a mile if you have discerning tastes

I completely agree. But last.fm doesn't do human review either, right? OTOH, I think the "radio" feature on spotify is wildly underrated. I used to spend hours compiling playlists for different moments/social settings/moods (still do, because it's fun), but picking any song/album/artist and then getting an endless stream of similar music is an amazing feature. Unfortunately, sometimes it screws up and will suggest me the same songs over and over again and there is no way to get a little more control over the algorithm.

I came to hate the Superstar cover by Sonic Youth and "I'm So Tired" by Fugazi for those reasons. Great songs, but they just kept coming up as the first recommended track and I can't stand hearing those intro piano chords on the latter anymore!! According to my last.fm [0] I played it 50+ times and I don't even remember once seeking out that song or putting it in one of my playlists.

https://www.last.fm/user/mahathu

> But last.fm doesn't do human review either, right?

No, but to be fair, that's not my use-case. My use-case is going to forums of like-minded individuals and saying "Hey, I need more music like _____. Take a look at my last.fm profile and suggest something I'd like."

I use it the same way book lovers use their Goodreads profile, I suppose?

There was a time when last.fm recommendations were uncannily good. I haven't used the service for a very long time so I can't say much about what they're like now or how they compare with stuff like music.youtube, Spotify or Pandora, but I recall when they were the darling "it" thing as far as music goes.

"Audio-Scrobbling"! It was hot off the end of the web 2.0 craze along with flickr and delicious. Good times. We weren't expecting the evil parts yet. I dropped off probably around 2012 or earlier.

As much as I enjoy good recs and an endless, mildly interesting playlist, the surveillance capitalism in the sauce is a real turn off.

This sounds like a fairly easy optimisation, actually. Whereas a human goes "Oh, so you mentioned A, B, and C, but not the otherwise popular D" and conditions on all that information, a simple recommendation system would just go "ah I see you didn't mention popular D, that must be because you don't know about them!"

Should be fairly easy to adjust the recommendation system so that it conditions on what you specifically did not mention, assuming that the most popular of that stuff you know about already.

A lot of people are not aware that Last.fm has a lot of these "Human recommendation" mechanisms. they are just kind of indirect. For exple, those humans could be

- People with whom you share recent top artists -> the "neighbours" feature

- People who also like that obscure favourite track of yours -> the "X listen to Y a lot" feature (or even just a delightful Shout on that track)

I often just convert the Loved ones from their "Top 100 Tracks" into a Spotify playlist (I may even filter it by genre beforehand)

The whole point is you don't have very similar taste to them. Still, you can find some totally unexpected gems.

Pandora was the best for recommendations. They stopped it being available in the UK over a decade ago, made me sad!

Two things I like to see trailed in music recommendation algorithms would be:

1. Associations happen at a song level. At the moment when artists are recommended you get what I call the "Red Hot Chilli Peppers" effect. That is, the artist that basically everyone seems to have in common in their playlists is RHCP. Instead, I think the recommendations should be more granular, at a song level. Artists can diverge quite wildly in style between individual songs (Ween being an extreme example of this). Take for example Planet Caravan by Black Sabbath (https://www.youtube.com/watch?v=SvrOzYtnLMA) this is great song but wildly different from say "War Pigs". In fact, it's a dubby, groovy slow jam.

2. Weight follow on plays more highly. So taking War Pigs in the point above. I listen to Planet Caravan and it makes me think of this great remix of Wicked Game by Trentemoller (https://www.youtube.com/watch?v=ph4z3u7IwrM). A totally unrelated artist and genre yet somehow linked in my mind. My assumption is that people that listen to Planet Caravan would also have a high likelihood of enjoying the Wicked Game remix because it has a similar... vibe I guess?

Anybody want to collaborate on building a recommendation engine around these two points? I'd be more than happy to record what I listen to as training data for it.

> Weight follow on plays more highly

What if I listen to Black Sabbath and then my niece wants to listen to the Peppa Pig soundtrack after that? What if it's a party and everyone is queueing their favourite song?

The issue with recommendation engines is that they're dumb as rocks. They cannot tell that the Trentemoller remix might be related to War Pigs, but not Peppa Pig, without ears and a brain.

So, to be safe, they go for simple statistical aggregation which creates safe and boring recommendations that are unable to surface interesting connections.

What you are talking about (simple statistical aggregation) may be true 10 years ago. Deep learning is truly different.

I'm not saying it is "smart" or listen the way we are. But is not "dumb" either in that it does not just follow prescribed rules.

I personally prefer human curation. But I've heard many many stories of how spotify's AI sometimes surprises people with uncanny magical recommendations (and dumb ones too). And it is just a start.

Deep learning is not statistical aggregation, but still wouldn't solve the very example OP and I are talking about.

Deep learning is not smart nor is able to replace actual ears and understanding of music. Even a random recommendation system would be able to surprise once in a while.

Personally, I think Spotify's system is terrible and gives me a good suggestion every 100 wrong ones.

> Even a random recommendation system would be able to surprise once in a while.

True. And because there is no way to "measure" the quality, if you say it is now horrible your you, I'll take your word for it.

I don't believe in "General AI", But this is not a binary outcome. It WILL get better. How much? We'll see.

The only site that finds better similar artist matches for me is https://www.metal-archives.com.

I preferred Pandora (https://en.m.wikipedia.org/wiki/Music_Genome_Project) over last.fm, specifically because I thought they had better curation / recommendations.

Spotify and Youtube Music are pretty crappy though. It’s like we’re going backwards.

Well, last.fm has been around for 20 years, but at the outset they took no consideration that two "artists" might have the same name. And they still haven't come up with a better solution than relying on users to edit in things like "There are at least 4 artists with the name David Williams".

So, forgive me for thinking their recommendation algorithm may not be exactly state of the art either. There's no inherent reason a recommendation algorithm shouldn't be able to figure out that an omission is intentional.