What I'm looking for is better music discovery. This would require some form of machine learning/statistics/collaborative filtering, but basically, if I like a number of songs, I'd want the system to automatically recommend new songs/artists.
https://press.spotify.com/uk/2015/07/20/introducing-discover...
So it's pretty good if you are listening to the same music genre, however, it sucks if you like to change the music based on your mood, etc. I also noticed that it doesn't detect the language the music is in so I often get songs with lyrics in languages that I have no clue about.
Some of the songs have absolutely missed the mark, but there have been others I've found that make me go back and listen to the entire album they're from. Definitely have encountered different languages as well.
And the behavior you described doesn't sound "pretty bad". If you haven't listened to a particular genre in a few weeks, I think it's reasonable you don't get suggestions for it for the upcoming week of listening. But if you do start listening to that genre again, your next playlist will have it.
I know it seems like this is what you want, but for me I've never been satisfied with this simplistic approach to discovery. Taste is very complex, and for me saying to someone "I really love xylophone" and them recommending me a lounge jazz tune with xylophone will always fall flat. What most people don't realise is why they love certain instruments or tempos or genres etc. It's usually down to how they were introduced to these, which defines their archetypal music using these instruments for example. It's all contextual.
My context for liking xylophone goes something like this "I love xylophone, because in the late 90s I listened to a lot of Beastie Boys 'Hello Nasty' which has some really nice pieces of music with some great xylophone in them. Then hearing some Mulatu Astatke recently got me interested in xyolophone and ethio jazz in general, combined with a really cute reaction my girlfriend had to one of his songs."
The best recommendation engine I've seen is Spotify Weekly. It's really very good, but make sure you get in there and save a lot of artists and albums you like first
The radio from that converges to endless Foo Fighters and The Prodigy -- over half the songs!
It's not that I dislike them, but I don't particularly love them either and there is just no diversity on that station.
That said, if you haven't given the Spotify Weekly Recommendations a shot -- you should. Maybe it's because it has more data to work with than song/artist/playlist-specific radios, but for me that feature has been extremely spot-on. In fact, I hadn't used spotify for a while because I kept hearing the same songs, but now I religiously keep on top of my discoveries each week.
It seems like the radios point me towards the most common denominator and plays the most mainstream stuff that matches, while the weekly discovery lists come up with some very quirky stuff that I love.
I guess the radios can't have any "memory", it seems to generate close to one song at a time (hence a lot of repetitions), whereas the discovery list can be created 30 songs at a time and seems to go out of its way to find songs by bands that I've never listened to.
Subjective tastes tend to diverge too rapidly with machine learning.
If (fingers crossed someone is doing this) there was a really easy and non-spammy way for people to build a playlist using _any_ source material into something like muxtape (or opentape) I would use it. Until then, I will just continue to ask them for youtube links and bandcamp profiles.
There are some artists from whom I truly only like one single song from their entire catalogue.
Others, I like every single song, but that is super rare.
And I dont have the time interest or energy to actually attempt to tag or classify music and songs that I do like -- so I just pretty much am stuck listening to the same thing or actually getting individual recommendations from real people.
I've found those recommendations much better than Pandora or Google Music, and you can hook into it with many different services/players.
Last.FM was so ahead of the game it's mind boggling how they just seem to have fizzled out while Grooveshark, and Spotify came in.
Last.FM had a huge user base with great info on individual music tastes, had a functioning streaming service, a whole functioning ecosystem of music fans and musicians that just seemed to have layed dead in the water after it was bought out.
I used to think that same thing, but I've come to realize that most people don't care that much about music. It's just pleasant background noise to them.
It should set it to highest available, or let it be an option.
I like it otherwise!
On an unrelated but interesting note: I'm still trying to figure out their recommended algorithm, it seems to be a mix of tags, and cross-referencing your account with who's liked/played a song. What I don't understand is if I listen to a folk song, it will generally play another completely unrelated folk song but with no folk identifiers (tags). I've concluded it's magic.
If I catch up on my SoundCloud stream (posts/reposts) I'll switch over to somebody's list of likes. Some of my favorite producers are buried in their passion for music 24/7 and have upwards of 1000 likes to listen through.
Recommended tracks are nice, and it's something to fill the void if you are low on content from people you follow, but in my opinion it's not that much better than going out and seeking new music and artists in other ways. I'm sure as SoundCloud improves the algorithm though it could automate some of the behaviors I'm doing manually (e.g. playing content from and following users whose content is liked/reposted by the users I follow). It might be doing this already, if maybe only indirectly, I guess I just like having control. One of the most annoying things I'm afraid it could do is promote sponsored music. I love listening to music that people are passionate about, not music that is funded by large amounts of money.
Re: recommendations from blogs, did Hype Machine do anything for you?
I've no idea if that would still be the case as we're looking at about 5+ years ago now when I used it.
Discover Weekly is a very good replacement, though, even if it's not the same.
In fact, one of the biggest issues I have with current methods is that they recommend me more of the same. When I look for new music I want - to some extend - something that is unlike what I've heard before.
Say you listen to Blue Lines by Massive Attack. Chances are, the recommendation engine will recommend you Portishead or Morcheeba. (User likes genre trip hop? let's play more trip hop). But maybe I'd like to explore different forms of UK rap music now...
Last.fm [1] recommends more Massive Attack, or "Aftermath" by Tricky, followed by Portishead. Which is fine, as you said "chances are" that's what's wanted. If you wanted UK rap, you at least used to be able to search for the tags "uk" and "rap", but I can't see that on the new website...
This service[2] uses the Last.fm API to make a Spotify playlist based on a track.
In case of my example such a search would work. However, I guess there are lots of cases where a user doesn't even know what s/he wants. That is: "I want more of the same but it should also be very different"
Now that's quite a task for an algorithm! Also, making suggestions based on empirical user data is rather hard as people are highly emotional, especially when listening to music. Thus, people (in general) are not very predictable when it comes to music.
Individuals might have some core principles when choosing music so that perhaps individual algorithms for individual users could work but coming up with an acceptable one size fits all solution is rather impossible, imo.
Some people are happy when the algorithm suggests another top 40 song they haven't heard yet. Others will loose it when they get more metal songs but none of them is teutonic thrash metal [1]
Now we could empower people by making apps like spotify scriptable so that everyone could refine their recommendation algorithm. But realistically that is not what your average user wants...
Perhaps one could improve the situation by asking the user a few questions when s/he first starts the application. That way the app can choose the best fitting recommendation algorithm from a range of algorithms in the background.
[1] Apparently, this is a thing: https://en.wikipedia.org/wiki/Heavy_metal_subgenres#Teutonic...
Those nice little lists Spotify helpfully shows are almost guaranteed paid-for inclusion (apart from the chart-style lists, to a degree, but that's another ball game). Labels/Record Companies/Promoters etc are almost certainly paying for inclusion there as it drives huge awareness and subsequent merchandise, ticket etc sales. Much like radio airplay, where the station controls the programming, this is where Spotify gets access to the same revenue stream.
So giving me perfect recommendations filtered through an exclusion list for which, let's be honest, the maths and data science for have long been sorted out via standard ad-tech and e-commerce recommendation engines, is simply not going to aid in the value proposition for a nicely curated list they can charge to be in.
You will never discover anything really new, your experience will be narrowcasted. Avoid this and let dj's, friends, musicians, people, and just surfing around the internet help you find new music.
Whenever I hear someone excited about automated music discovery a little voice goes off in my head going ..."ohh no".
But if you want to find hits in a genre + time period that you don't know very well, recommendation engines are pretty good, and you don't need to have DJs for that.
For example, do you think someone like DJ shadow who has spent tens of thousands of hours digging in empty, old, dusty basements finding and curating music over a lifetime has the same insight as a dicovery algorithm?
It's not even close to a comparison, the algorithms are lame compared to actual experience and expertise.
I spent 10+ years in the music business as a "buyer" and was later part of a successful music start-up, I can tell you with a resounding "fuck that" to auto discovery unless you like water downed with a side of boring.
Radio DJs, on the other hand, have any number of forces affecting their song choices (to the degree the role exists at all anymore), and all in all they're just as much a black box as an algorithm. Sure, we all had radio shows we liked for some time, but those DJs changed and we found something else (notwithstanding people who have been listening to "Renee and Crazy Pete in the morning" for 20 years).
Even with all of this, I suspect there's a discrete economic or mathematical reason that profit-oriented discovery engines regress to a mean and will always be trying to recommend the latest AdeleBieberNational. They may not, right away, but a few clicks deep in the sidebar and you find LL Cool J or Foo Fighters popping up.
Many artists are starting collectives which publish albums with a variety of artists approximately monthly, which also is great for discovery.
Of course this only works if your preferred genres are there - if you like future funk, nightcore, and hip hop you're golden.
Moodlogic worked on my library, I could choose any song, then it would craft a variable length playlist based on that song's mood and instruments. There was a crowd-sourced set of metadata, and I was able to fill in the gaps or correct anything I didn't agree with.
If anyone knows of anything along these lines, I'd be grateful to hear about it.
Unfortunately, it requires to be running at spotify-like scale to do something useful, have a large enough library and gather feedback quickly enough :( oh well. I will continue dreaming about it though.
https://developers.google.com/youtube/v3/docs/search/list#re...
For new stuff, I'm fine with a combination of best of 20## lists, following some interesting soundcloud accounts, and idle searching the internet / various subreddits.
As a replacement, I subscribed to popular blogs (popjustice, stereogum...) playlists on Spotify.
The UX is utter shit, but the suggestions are great.