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I starting making one music-list a year some time in the 80s, before I really knew enough for there to be any sense to this activity. For a while in the 90s and 00s I got more serious about it, and statistically way better-informed, but there's actually no amount of informedness that transforms a single person's opinions about music into anything that inherently matters to anybody other than people (if any) who happen to share their specific tastes, or extraordinarily patient and maybe slightly creepy friends.  

Collect people together, though, and the patterns of their love are sometimes very interesting. For several years I presided computationally over an assembly of nominal expertise, trying to find ways to turn hundreds of opinions into at least plural insights. Hundreds of people is not a lot, though, and asking people to pretend their opinions matter is a dubious way to find out what they really love. I'm not really sad we stopped doing that.  

Hundreds of millions of people isn't that much, yet, but it's getting there, and asking people to spend their lives loving all the innumerable things they love is a more realistic proposition than getting them to make short numbered lists on annual deadlines. Finding an individual person who shares your exact taste, in the real world, is not only laborious to the point of preventative difficulty, but maybe not even reliably possible in theory. Finding groups of people in the virtual world who collectively approximate aspects of your taste is, due to the primitive current state of data-transparency, no easier for you.  

But it has been my job, for the last few years, to try to figure out algorithmic ways to turn collective love and listening patterns into music insights and experiences. I work at Spotify, so I have extremely good information about what people like in Sweden and Norway, fairly decent information about most of the rest of Europe, the Americas and parts of Asia, and at least glimmers of insight about literally almost everywhere else on Earth. I don't know that much about you, but I know a little bit about a lot of people.  

So now I make a lot of lists. Here, in fact, are algorithmically-generated playlists of the songs that defined, united and distinguished the fans and love and new music in 2000+ genres and countries around the world in 2019:  

2019 Around the World
 

You probably don't share my tastes, and this is a pretty weak unifying force for everybody who isn't me, but there are so many stronger ones. Maybe some of the ones that pull on you are represented here. Maybe some of the communities implied and channeled here have been unknowingly incomplete without you. Maybe you have not yet discovered half of the things you will eventually adore. Maybe this is how you find them.  
 

I found a lot of things this year, myself, some of them in this process of trying to find music for other people, and some of them just by listening. You needn't care about what I like. And if for some reason you do, you can already find out what it is in unmanageable weekly detail. But I like to look back at my own years. Spotify's official forms of nostalgia so far define years purely by listening dates, but as a music geek of a particular sort, what I mean by a year is music that was both made and heard then. New music.  

I no longer want to make this list by applying manual reductive retroactive impressions to what I remember of the year, but I also don't have to. Adapting my collective engines to the individual, then, here is the purely data-generated playlist of the new music to which I demonstrated the most actual listening attachment in 2019:  

2019 Greatest Hits (for glenn mcdonald)  
 

And for segmented nostalgia, because that's what kind of nostalgist I am, I also used genre metadata and a very small amount of manual tweaking to almost automatically produce three more specialized lists:  

Bright Swords in the Void (Metal and metal-adjacent noises, from the floridly melodic to the stochastically apocalyptic.)
Gradient Dissent (Ambient, noise, epicore and other abstract geometries.)
Dancing With Tears (Pop, rock, hip hop and other sentimental forms.)  
 

And finally, although surely this, if anything, will be of interest to absolutely nobody but me, I also used a combination of my own listening, broken down by genre, and the global 2019 genre lists, to produce a list of the songs I missed or intentionally avoided despite their being popular with the fans of my favorite genres.  

2019 Greatest Misses (for glenn mcdonald)  

I made versions of this Misses list in November and December, to see what I was in danger of missing before the year actually ended, so these songs are the reverse-evolutionary survivors of two generations of augmented remedial listening. But I played it again just now, and it still sounds basically great to me. I'm pretty sure I could spend the next year listening to nothing but songs I missed in 2019 despite trying to hear them all, and it would be just as great in sonic terms. There's something hypothetically comforting in that, at least until I starting trying to figure out what kind of global catastrophe I'm effectively imagining here. I'm alive, but all the musicians in the world are dead? Or there's no surviving technology for recording music, but somehow Spotify servers and the worldwide cell and wifi networks still work?  

Easier to live. I now declare 2019 complete and archived. Onwards.
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