That's because the recommendation engine that Last.fm used back in the day was made the incredibly expensive way: the entire corpus was hand-tagged and cross-linked by humans atop an enormous CDDB. Last.fm, Audioscrobbler, and MusicBrainz (the association engine) were all linked together.

One really annoying example of YTM's algorithm is it (or whoever works on it) doesn't understand that a genre can have diverse sounds and instruments, so it will recommend songs that all sound the same.

Like if I start listening to house music, it will just recommend 100 songs that have organ 2 [0], even though house music is more diverse than that. Then it forces me to thumbs down the music, which also isn't what I want to do, because I have no idea what effect it's having on my recommendations. Is it just going to stop recommending house music altogether? Is it going to stop recommending songs with organ 2? Is it smart enough to understand that I just want less and not none? I do like organ 2, I just don't want to drown in it when I'm trying to find new music.

Or I will thumbs up a phonk song and it it just floods me with phonk remixes of pop songs.

Last.fm, on the other hand, seemed to have some way of towing a line of different enough without going too far. Both YTM and Spotify algos just do cookiecutter similarity.

[0]: https://www.youtube.com/watch?v=Iq61C8gndjM

Great summaries. I also have a real affection for my last.fm discovery, and I think it had everything to do with "deep discovery" going deep into the related artists pages. It really shaped my relationship to music and my love of music discovery and I sometimes find I don't click with people whose idea of discovery is The Algorithm(TM).

I tried to import my music life into Google Music, uploading my lifetime of libraries there. When they wound that down I just lost trust in online services and now do it through Nextcloud, which honestly is pretty awesome imo. There's no algorithmic suggestion for better or worse, but none of the "who ordered that" style assumptions imposed on you by the system like those you outlined above.

I switched to Apple Music to save some money and I find the curation and the recommendations to be significantly better than Spotify.

Cannot call lastfm algorithm advanced in any sense. Just opened Amon Tobin page: "similar artists: Kid Koala and DJ Kush", which is an impressively shallow understanding of the last 20 (!!) years of his life, and this happened with almost every artist on the platform, because the average sum of tastes of every listener does not exist in reality. E.g. in the case of Amon Tobin, Kid Koala is the average of similarities between early albums and recent releases, which is just not true, his music cannot be averaged throughout his career. I love my Web 2.0 youth, but the average similarity algorithm doesnt deserve praise. Its not better, its nostalgia and lack of faang-style unlimited greed which confused with better quality

Edit: of course spotify-style recommendations are much much worse, I just mean that lastfm doesnt have good algorithm either because artists are not consistent in releases. What is an average between electronic cult classic "The last resort" and every other Trentemoller album in strict indie rock style? This average does not exist

I'm 90% sure that music labels pay to "put their thumbs on the scales" with these recommendation algorithms in order to push their "hot" artists. I wonder how many of these problems are a result of that.

The other frustration I’ve noticed is that they key in very heavily on artist and specific “genre” designation as what feeds the recommendation, which is actually quite bad for anyone who likes experimental work.

I understand that if your recommendations are based on “people who like this also tend to like that” then you’re right in the strike zone. But that approach is basically agnostic to any property of the music itself. Suppose there’s a rock band that released a specific song where they’re experimenting with a new style that has an atypically (for them) funky/jazzy influence. If I say I want more songs like that I mean songs that fuse rock/jazz/funk, not more songs that fans of [rock band] are into.

I still think for new music discovery Pandora’s approach remains the best if you really curate a station for yourself. Apple Music has been good for creating very listenable playlists though, and their new AI playlist generator has been very fun. Surprisingly, YouTube also seems to have some secret sauce where they recommend a lot of interesting stuff that I’ve genuinely never encountered before. I suspect this is because there’s a lot more amateur and experimental artists on there doing weirder stuff and it’s able to find audiences for those in ways that the music-focused services have less visibility into since their catalog is so focused on stuff from the recording industry.

I just remembered that I met one of my best friends to this day through Last.fm. It was 2009 or so, and you could leave messages on concert pages.

I posted asking if anyone wanted to go with me since I didn't want to go alone, and she sent me a message.

Good times.

I love clicking on a random person's profile and seeing that they're actively using the account (as in, I can see they're currently listening to a specific song!) they've had for 20+ years. So cool.

I second this. I started as an Audioscrobbler user before the Last.fm merger. I have tracked nearly every track I've listened to for 21 years. It's awesome seeing how my habits have changed over the years.

As a long-time user, I do enjoy seeing how my tastes have changed over the years and which artists and albums I play the most. I also tend to agree that the Last.fm recommendation engine was perfectly fine for my use case compared to the algo that Spotify uses now. https://www.last.fm/user/wyclif

Same. I have one or more gaps in there which I wish I could go back and correct. I feel like integration with the service is a must for any music thing I pick up, the most recent being this year, resurrecting my old iTunes library via Navidrome.

I think RateYourMusic also allows for commenting pretty much on everything (and it also extends to film and, IIRC, videogames).

It might not be as friendly as last.fm on the surface, but it is surely richer in content, and more diverse. It's a gem, and I hope Sonemnic (?) doesn't drive it to the ground.

More than a feeling.

Pretty much all the machine learning recommendation engines that emerged in the Netflix era were doomed to collapse under their own weight over time for non-mainstream users because the some limited number of mainstream modes dominate as most statistically "optimal" across the total user pool. These algorithms are best in the early days, when they're still exploring the content space for good novel fits but eventually get trapped into deep, boring grooves that work really well for tons of non-discriminating users with similar tastes.

Separately, in real commercial terms, they're all fundamentally poisoned by business model objectives of highlighting cheap content or servicing partnership/advertising deals, etc. And that problem also becomes more and more prominent as the companies running them grow and become more influential and as they need to squeeze harder and harder for revenue growth.

It was basically just a long, winding, wildly expensive road back to broadcast radio programming.

It was a good run for a while, but we're long due for a new model.

Yep, member since May 21 2005 here, still scrobbling with Spotify. Don't think I've ever used any of the radio features on the site, really; even back in the 00s all I used were the WinAmp/Foobar plug-ins.

You could scrobble to Last.fm or ListenBrainz to track your personal library listens.