7 ms·

Still use last.fm. Most "You might like this" algorithms go straight for the low-hanging fruit, and often fail to take any kind of nuance into account. I can't

Still use last.fm. Most "You might like this" algorithms go straight for the low-hanging fruit, and often fail to take any kind of nuance into account. I can't tell you how many of the streaming services will see my Black Sabbath play history and immediately recommend, "If you like Black Sabbath, you should love...Slipknot!" But I've never had a real person make that mistake, because a real person who looks at my last.fm history and has an understanding of the genre says "Gee, this guy has plenty of Black Sabbath, Iron Maiden, and tons of doom metal on his list, but doesn't have Slipknot, Korn, or Pantera in his history. Maybe that's intentional." Human review and recommendation still beats algorithmic recommendation by a mile if you have discerning tastes.

powersnail 4y ago
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!
beezlebroxxxxxx 4y ago
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.
- CrypticShift 4y ago
  It depends on how the recommendation system defines 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.
- kixiQu 4y ago
  How do you find the https://everynoise.com/ https://everynoise.com/ categories to fare at this?
  - CrypticShift 4y ago
    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)
    - kixiQu 4y ago
      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/ https://www.furia.com/), so I'd've expected that to work decently well.
SimonPStevens 4y ago
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.
- lordgrenville 4y ago
  The term for this is https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Collaborative_filtering
- AdmiralAsshat 4y ago
  > 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."
  - nemo44x 4y ago
    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.
abawany 4y ago
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.
mahathu 4y ago
> 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 https://www.last.fm/user/mahathu
- AdmiralAsshat 4y ago
  > 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?
crispyambulance 4y ago
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.
kqr 4y ago
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.
CrypticShift 4y ago
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.
maxrev17 4y ago
Pandora was the best for recommendations. They stopped it being available in the UK over a decade ago, made me sad!
Rodeoclash 4y ago
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 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 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.
- sph 4y ago
  > 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.
  - CrypticShift 4y ago
    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.
camel-cdr 4y ago
The only site that finds better similar artist matches for me is https://www.metal-archives.com https://www.metal-archives.com.
rapind 4y ago
I preferred Pandora (https://en.m.wikipedia.org/wiki/Music_Genome_Project 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.
vintermann 4y ago
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.

powersnail 4y ago

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!

beezlebroxxxxxx 4y ago

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.

CrypticShift 4y ago
It depends on how the recommendation system defines 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.
kixiQu 4y ago
How do you find the https://everynoise.com/ https://everynoise.com/ categories to fare at this?
- CrypticShift 4y ago
  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)
  - kixiQu 4y ago
    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/ https://www.furia.com/), so I'd've expected that to work decently well.

SimonPStevens 4y ago

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.

lordgrenville 4y ago
The term for this is https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Collaborative_filtering
AdmiralAsshat 4y ago
> 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."
- nemo44x 4y ago
  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.

abawany 4y ago

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.

mahathu 4y ago

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

AdmiralAsshat 4y ago
> 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?

crispyambulance 4y ago

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.

kqr 4y ago

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.

CrypticShift 4y ago

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.

maxrev17 4y ago

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

Rodeoclash 4y ago

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 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 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.

sph 4y ago
> 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.
- CrypticShift 4y ago
  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.

camel-cdr 4y ago

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

rapind 4y ago

I preferred Pandora (https://en.m.wikipedia.org/wiki/Music_Genome_Project 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.

vintermann 4y ago

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.