Last.fm turns 20
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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.
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.
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)
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.
"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.
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."
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.
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.
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.
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.
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.
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"
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
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.
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.
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 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.
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?
"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.
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.
- 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.
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.
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.
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 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.
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.
Spotify and Youtube Music are pretty crappy though. It’s like we’re going backwards.
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.
All of that drifted away as I got older, and the dawn of streaming services like Spotify came onto the scene. I'm not sure where my music is now, probably on a hard drive somewhere, dumped amongst other junk.
I think spotify used to come with last.fm support but I think I just lost interest in the whole thing, I don't consume music in the same way as when I was a younger man.
EDIT: Just logged into my last.fm account and it looks like the scrobbling still works from spotify, so it's been scrobbling all this time, probably for 10+ years without me logging in!
are you surprised by your last.fm statistics? will you delete the account?
I've used Spotify (and other streaming apps) for years. I finally got tired that there was no streaming app that:
* Worked well with Sonos
* Provided high quality music
* Had good automatic caching of music *note below*
I tried Spotify, TIDAL, and Apple Music before giving up and putting my library on Plex + Plexamp.
*note*: Why is Spotify SO BAD when it comes to caching. It's so strange. There are so many situtations where it doesn't work, but it should.
I have the entire contents of an album (but not the album itself) on playlist. The playlist is downloaded offline. Spotify doesn't let me search or play the songs in the album unless I navigate to the playlist.
If I just played a song on data, and I lose my connection and try to re-play it, Spotify won't let me! Does it really not maintain a cache of songs that I played three minutes ago?
Search becomes entirely useless when you're transitioning from WiFi to mobile data. This happened to me _every_ _single_ _time_ that I would get into my car and start music before driving. Spotify would refuse to let me do anything until I disabled WiFi.
Spotify has to be one of the worst widely used applications.
My beef with Apple Music is that they can’t even be bothered to cache the whole song that you’re listening to.
The number of times that I’ve started playing an album on my phone then had it stop the instant I drive out of WiFi range. You had a whole minute to download three megabytes over fibre and didn’t even bother. 5G is ubiquitous and perfect everywhere, right? It sure is in the Bay Area where all us apple engineers live, so how about we cache, say, one second of audio in case somebody goes through a tunnel or something.
I did absolutely love the Dolby Atmos music though. I hope that catches on.
I still have that 100GB+ music collection on my laptop, all curated by myself (with the help of last.fm), organized in folders and with proper ID3 tags, which I spent years building when I was young and had no better things to do.
And you know what? This is the very reason why I never got on spotify or any of that stuff. I just don't see why I would give up the perfect (for me) collection and use some online service where I have no guarantee that I could even listen to the same track a year later.
I have music that I like and listen to, and I like to keep listening to it.
In addition to a large mp3 collection, I am also buying CDs and vinyls. Everything I can to build my own music library. The only online service I use for listening is YT, which has some unique content (live shows that are not found as albums) - but even these, I tend to download and save, because you never know...
Then I just lost interest in updating my music library. Didn't want to pirate any more, I did buy a new CD every now and then but couldn't be bothered to do the EAC ripping + tagging even for them. So I kept listening to the old collection without hearing much new music. And then I switched to Spotify to get the new music conveniently.
It was just perfect at everything, was it finding new releases, discovery obscure gems, or play your favorite things all over. It was so magical that you could hardly believe it was computer generated.
Also, it was a wonderful "sane" social network where music and only music was at its heart: no vanity metrics (eg: counting likes) / vanity egos (eg: influencers) or purely material interests (eg: make money from this or that).
Anyway, it's sad it lost the streaming war pretty soon. Maybe it was just too genuine to compete with services driven by dark patterns, suspicious agendas, and mostly, greed.
Wait, is this a metaphor of the old internet versus the present state of digital affairs? Or am I just getting nostalgic here?
I don't know. Long live Last.fm!
111,920 Scrobbles from 9,137 Artists since Jul 2006.
There is a place for both: Streaming services and DB/SN sites. they don't have to be the same.
I even hope for more segmentation (and interoperability) in the future: Companies that only provide the raw music. Others that build the UI/UX (players...) and still others that provides additional services (Social networking, Data related...)
Last.fm lost not because it is too genuine (CBS is one of the largest media conglomerates) but because it got lazy and failed to leverage its data to create more value. The truth is people are just as Lazy when it comes to music (organize, discover…), _so I wonder how much you can really grow in this space.
> all the album by this label
label:[label name] is helpful. There are better options. this one comes to mind https://www.lbl.fm/
> all the songs produced by X
If you open the credits of a track, you will find that some writers (no producers) are hyperlinked to a page where you can listen to all their songs as a playlist.
> all the songs where in Y plays drums. All the albums recorded at some studio in the year
A Hint: try you luck with a search for a known label, producer or even drummer, or studio, and you will be surprised by how people are creative/meticulous with their public playlist’s choices (the human pool is just getting way too large, 400 millions and counting)
Also, you can take a look at https://spicetify.app/. It is a marketplace for extending the desktop app (some data related add-ins in there). it is still very young and hacky but IMO it is the right direction.
https://news.ycombinator.com/item?id=2775330
I enjoy using it as a statistics aggregator as well. Last.fm definitely gets credit for capturing the primal sense of achievement with scrobbling. People I know don't really use it as a social network other than friending each other, but even that is sufficient because it allows you to peek into their current musical tastes. It's sort of elegant how Last.fm's experience is pretty much just collecting data and displaying it nicely; no need for apps, uploading user content, or location-based gimmicks. Just listening to your music library is generating content enough.
Knowing that I generally like the music I have, I have found some huge success with a simple change: I do not in any way allow myself to influence what music is playing.
It's very easy to do. The first version of this was done in my woodshop, because it's creepy back there: a cheap amp with a 64 gig SD card plays the first 64 gig of my music on a loop. I believe it's about a 3 week loop. Whenever I go out there to get a screw driver or drop of a bucket of old paint, there is some OC Remix collection of Legend of Zelda, or some Cowboy Bebop, or their ilk, playing at a reasonable volume. It keeps ghosts out and really increases my pleasure of being out there - just enough distraction to be able to think.
Version two is my now permanent work-from-home office. I have been exploring these Raspberry Pi audio distributions. Volumio, and now a fork of rune audio called rAudio-1. At first I was allowing myself to fall back into the same trap: selecting a folder on a NAS share of the music, but then I rediscovered Web Radio. I left it on a classical music station for a little over 4 weeks, just soft piano music at about 40 dB, barely detectable. My work microphone can't (or wont) pick it up so I let it play right through meetings.
It took me that four months to realize I could install my own web radio station of my own music. With icecast2 and mpd running on an underused virtual machine, I can leave it playing all of my music on a single stream available to only my rAudio-1. Coupled with a 60 dollar add-on DAC to (significantly) clean up the audio quality, and a 30 dollar amplifier that has only a volume knob, my situation is now perfect: I can either listen to what is playing, or I can not. If I don't like what is playing, then I either listen to it anyways, or try later. I'm listening to music 40 hours a week again, and am better for it.
For all their AI and ginourmous models, they mostly seem to be stuck on "what you listened to yesterday".
Edit: by cheap knockoffs I literally mean artists who look and sound like the original. Eminem knockoffs come to mind as particularly striking.
Nowadays I use Deezer and it's definitively worse.
I miss the last.fm radio so much!
It’s nice to fill gaps or deep cuts in a more obscure style you like.
It just wasn’t for me in the end but I can see how it’s appealing for other people. For me it will always be a nice little reminder of the mid 00s along with stuff like indie music and the mighty boosh.
I’m surprised/happy to see iLiKETRAINS as your top track.
Currently at 94,000+ scrobbles since June 2007.
You can scrobble from almost anything including the Last.FM client, WebScrobbler [1] and some streaming apps have it built in like Spotify.
Scrobbling has almost become a religion to me, to the point I found solutions to scrobble my Vinyl listening and consider scrobbling capability in a player as a make-or-break feature !
I assume you use the Discogs app for scrobbling your vinyl? I was really pleasantly surprised when I found that feature :)
I actually didn’t know the Discogs app supported scrobbling, I currently use an open source app called “Finale”[1] that does the job pretty well. As soon as I can get my hands on a RaspberryPi, I have plans to make the whole process automatic.
As a small side project I made a CLI tool in Python [1] to retrieve music collages from tapmusic.net [2] (uses your Last.fm scrobbles to create collages of your most listened to music).
Check them out if you have a second!
I think its decline will probably correlate with people's transition from MP3s to streaming services. It was pretty sad when that breach happened, as that felt like a nail in the coffin.
https://techcrunch.com/2016/09/01/43-million-passwords-hacke...
That said, I do occasionally stumble upon Last FM in search results and it's slightly surprising and slightly pleasant to still see it around.
The 'burgeoning' Discord presence (400,000 total users) doesn't feel like a lot however, it still feels like a niche interest group.
I don't check it very often any more but for some reason I still like to look at the graphs from time to time, even if I don't really hunt down recommendations based on that.
Still brutally pissed that after more than 10 years with the same account, and even with Premium, Reddit wont let me change my old short name, which is related to my deadname as a trans person.
Of course; support requests have been completely ignored.
Contrast thia the regular complains around products like pinboard/evernote. Pinboard gets flack for not changing, and evernote for chasing people with useless features.
I love last.fm, it still has the vibe of the healthy internet. It is cool to dig into my listening history. Last fm suggestions and recs are still the best. The wikis are great for fans.
I miss the times where i was finding friends by shouting to strangers profiles. Just in the love of music.
Long live last.fm!!
While Last.fm failed to capitalize on early innovations like the social media features and algorithmic online radio, the "scrobble" feature oddly enough stood the test of time. Even though it was the pre-smartphone age, developers built unofficial applications to let you log your listens from almost anywhere: iPod/iTunes, Windows Media Center-compatible MP3 players, and eventually any audio player that ran the hacked Rockbox OS, including the iPod 5G.
CBS really screwed this up. To their credit they kept the API alive, so other could somehow innovate with the data.
GNU Libre.fm worked fine for years tho! Nice to have an archive of data just sitting on there. (Lack of hands to work on it to get other features like dumps and more analysis never panned out tho).
But since the pandemic my listening habits on mobile where most of the scrobbling was being done from have changed completely and there's limited support on windows / no long available in things like WinAmp etc, so it's just not a thing anymore. Kinda sad.
GNU social was a spin off, later merging with StatusNet and later Mastodon was a rewrite of GNU social in Rails.
https://git.savannah.gnu.org/cgit/librefm.git/tree/gnufm_ins...
I’m no longer involved with the GNU project so the site is in a weird state where the GNU docs are stuck in time. I do need to do a better job of pointing people to what’s out there though. I am considering a rewrite in modern PHP.
Last.fm was great - I wish I could work out why I left.
I recall him working on audioscrobbler as a 3rd year project, and was quite impressed with it, mostly by the fact that it actually worked and recommended me something I liked.
It maybe started as a plugin for one of the popular audio players at the time (name completely escapes me though).
Only other thing I remember was him being super passionate about it, and wanting to carry on development after uni.
The project has been packaged for a couple of distributions but you can also find it on github.
It’s no Rdio [RIP :(] but the recommendations from LastFM are probably the best I’ve encountered.
So many memories, discovered genres, met friends, found a subculture where I belong (more like the subculture found me with the neighbor system)
Shameless plug: I'm a firm believer that human recommendations are superior, which is one of the reasons I'm creating https://digs.fm, something like Goodreads but for music[1].
0, https://web.archive.org/web/20150227221251/http://www.last.f...
Happy Birthday!
These days, I would go as far as to claim that I grew to despise the figure of the music nerd. I never visit websites such as last.fm and RYM, and the mere sight of a copy of "In the Court of the Crimson King" is enough to ruin my day.
It's hard to explain.
Shame it's pretty much on life-support now and any new features seem to behind a paywall.
However, one pain point with all versions of the API is lack of full HTTPS. I've always been really confused why I'd still see HTTP traffic to the Scrobbling API despite giving it an HTTPS scrobbling URL, and what I found is that you authenticate over HTTPS to get a token, but then the API returns a submission URL that's always plaintext HTTP.
I believe you can still send it over HTTPS by rewriting the URL, and I know that this API was designed before the ubiquity of HTTPS, but it's absurd to keep doing this HTTPS --> HTTP redirect in 2022. Even 3rd party scrobbling services like libre.fm copy this behavior. When API docs give you a primary endpoint of a plantext HTTP URL, that's a huge red flag.
However, I do still love last.fm and still try to obsessively scrobble everything I'm listening to since I've discovered a lot of my favorite bands that way, and it's a great way to get recommendations of live music if you're ever traveling somewhere. I think the paywall has been there for a while, but I'm definitely guilty of not supporting it enough despite how much value I've gotten from it over the years.
The only thing I'd wish it'd do is update my play counts from my Sony Walkman NWA.
It's a great service and, as others have mentioned, it really does a great job of finding obscure and relevant suggestions on underground metal bands that I enjoy.
It's particularly good at high quality, relevant recommendations for new bands with little following.
And of course, discovering music similar to your musical taste, that really helped me to discover new bands.
Does Spotify to Last.fm still work for anyone?
A bit of a shame that MyMedia for Alexa (local music streaming for Alexa) doesn’t support it, but at least that’s only for the kitchen anyway.
the old service was really cool but it stopped. i have no idea wtf people are talking about when they say its '20 years old'. it literally ceased to exist.
i realize it may have been replaced with something else, but that something else bears almost no resemblance to what it was.
please help - a confused old man
It's a pretty useful service that nobody is going to pay for unless it comes included in a streaming service already.
if spotify bought it, they would almost certainly kill off scrobbles from outside music sources. of course they would promise this wouldn't happen, but we all know from repeated experience, over time the api would slowly be closed off.
Also, Spotify now has the data of more than 400 million active listeners. Last.fm has at best 20 millions of users (including a lot of inactive one).
At least within Canada, Neil Young's music has always been available on Spotify even during the period when he was feuding with the service about Joe Rogan.
Or was just within the US?
I’ve got like 30k songs played and discovered on last.fm and I stopped using it when they ended the service as it used to be years ago.
Scrobbling since 31 Mar 2006, wow that's fair few music phases. 18-34.
Back in the day practically all players (including spotify) had scrobbling support.