
Spotify’s Discover Weekly: How machine learning finds new music - kiyanwang
https://hackernoon.com/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe
======
Asdfbla
Sometimes I wish Spotify added a bit more 'noise' to their recommendations, so
to speak. If I don't listen to much music except Discover Weekly for a few
weeks, I (subjectively) find that what's recommended to me more or less sounds
the same after a while. Either they are afraid to insert new things that stray
too far from an optimal recommendation or they forget too much of my listening
history.

~~~
iddqd
I used to listen exclusively to Discover Weekly for a while. One week a finish
rap song made it to the list, and I skipped past it every time it came on.
Next week there was 2 finish rap songs. Then 5. Eventually half of my
discovery list was finish rap, something I have no interest of. Canceled my
subscription shortly after. Their algorithms are feeding themselves. I wish
they had a dislike button so I could at least give them some sense of
direction.

~~~
Cthulhu_
They do have a dislike button though, the thumbs down icon next to the play
controls (on the desktop version of Spotify).

~~~
lewapkon
It's there only if you listen to a radio, not to a playlist.

~~~
mtremsal
There's a way to consume Discover Weekly as a station instead of a playlist.
;)

~~~
slhck
How exactly? — Ah, figured it out:

\- Go to the DW playlist

\- Click the "..." options button

\- Then to "Go to Playlist Radio"

\- "Follow" the DW playlist radio

\- You can now up/downvote songs

~~~
emodendroket
Wow, what a great user experience.

~~~
youareterrible
If you had it all on screen you would talk of button fatigue. Users are the
worst.

~~~
emodendroket
Did you know Spotify also has a its approve and disapprove buttons in the
desktop app on opposite sides depending on what particular radio-like feature
you're using?

------
arkadiytehgraet
If there is at least one song that I like in Discover Weekly, then I consider
it to be a good mix. However, this happens once a month at best. After reading
the article and all three methods for recommendations, I cannot understand how
on Earth it comes up with its suggestions. Do I feed it bad data? Does it not
take into account the relative "weight" of a particular track, i.e. my
preference to listen to it multiple times, skip others and so on? It looks
like by considering these simple points it can be improved by a large margin.

~~~
ikornaselur
Yeah, I was surprised when I read the start of the article. On Mondays, I will
start the day by playing my discover weekly playlist and I'll usually have it
running throughout the day, saving 1-4 songs from it to my library. Sometimes
none.

I just checked and this week I didn't even save a single song for example.

I guess it doesn't help that I do listen to really different types of music
depending on what I'm doing. If I'm at home, I'm not listening to the same
thing as if I'm at work (developer). When I'm reading, I'll also be listening
to something different from normally. I wonder if that makes my discovery much
more "basic", since it's a mix of pretty much everything.

~~~
Pigo
I don't really use Spotify. But this is why I like Google Play Music, because
the playlist suggestions are categorized by what you're doing (rockout at the
gym, christmas, working-chillout, working-fun).

But honestly you can't beat just going to youtube for a live stream if you
want some chillout-ambient music while you're programming. There's so many
talented dj's out there culling new music.

~~~
jefffan241
Spotify has playlists for the same things you listed (minus maybe christmas,
but I'm sure they do). I've seen the others though listed.

------
Fabled
In case you didn't know, Spotify categorizes music into genres behind the
scenes. You can use this site to find out what genres your favorite artists
are categorized as:
[http://everynoise.com/engenremap.html](http://everynoise.com/engenremap.html)

You can use this information to then check out Spotify's auto-generated
playlists for each genre. They have at least three types for each one: "The
Sound of <genre>", containing definitive representation of the genre, "The
Pulse of <genre>", containing songs that fans of the genre listen to now, and
"The Edge of <genre>", with unpopular songs (not necessarily of the same
genre) that fans of the genre listen to.

This has been a great way for me to find new music that I like, especially
"The Pulse". I even created a small script that takes a spotify playlist as
input, parses all the artists, converts to genres, and creates a new playlist
with "recommendations" based on the Pulse playlists, with each genre
represented based on its percentage in the initial playlist.

Basically, I never use Discover Weekly, because I know it will eventually
converge just like all my Pandora stations that cycle through the same 20
songs after a few months.

~~~
probably_wrong
> You can use this site to find out what genres your favorite artists are
> categorized as

That's my problem right there: my favorite song right now is a musician's
piano cover of one of his own songs. His music is usually electronic, which I
don't like, but I love this one song. So Spotify will recommend me electronic
music from other artists, which of course does not fit my song.

Repeat for every author. I liked one song from a German musician, and now half
the recommendations are German music. While I can see why network relations
make sense, I wish I could say "give me a similar song, not a similar artist".

~~~
eru
Pandora works song-by-song (and is not bound by genres).

~~~
probably_wrong
It's also not available outside the US :-/

~~~
eru
Alas. (Though, it is available in Australia, if that's any consolation.)

Proxies work.

------
nobleach
I find that my music tastes can vary too much for Spotify. While working, I
mostly listen to Electronic music. But I can also take a hard right turn and
listen to Hall and Oates... or Fleetwood Mac, or Steely Dan. Then some form of
80's throwback. But as far as recommendations go, I really only want to expand
in that first genre of Electronic Music. I don't want Jethro Tull to be
recommended to me. My feed used to be full of really cool stuff, but I find
that now I have several lines of "Because you listened to one song by The
Pointer Sisters...."

~~~
hammock
Don't you have Daily Mixes? These are smart playlists created by Spotify
organized roughly into genres, and you have can up to six - depending on how
diverse your listening habits are. Right now I have:

1 Electronic, 2 Pop, 3 R&B, 4 Indie, 5 Rap, 6 Country/Americana

Note they aren't named like this, I did that. Spotify identifies them only by
a number and a few of the artists contained within. You can find them in the
app on your Home screen or under Your Library > Your Daily Mix.

~~~
ar-nelson
These solve the problem somewhat, but, in my experience, they _only_ play
songs I've already listened to. So listening to songs outside the genres I
want recommendations for will still ruin Discover Weekly's usefulness as a
discovery system.

I used to love Discover Weekly, and found a lot of great music through it, but
it's gotten less useful over time. A few things that would improve it:

• Multiple Discover playlists sorted roughly into genres, like the Daily Mix
but only containing never-before-heard music

• Treat Discover Weekly like a radio station, with thumbs-up and thumbs-down
buttons

• Once I've listened to a track a few times, _stop suggesting it._ At this
point I can usually predict what my Discover Weekly playlist will be: all of
the songs that have showed up on it before, but that I didn't like enough to
click "+" on.

~~~
anidesh1512
They intersperse some new songs between my already saved songs.

~~~
travmatt
I prefer them for exactly this reason - I’ve learned that I actually prefer
less novelty in my playlists than I previously assumed. Spotify seems to
understand this, and incrementally alters my playlists, moving through tracks
and doing a good job of removing tracks that I skip continuously. I’ve noticed
these playlists actually do a good job at discovering music for me within each
genre

------
markussss
The biggest problem with the Discover Weekly is its inability to understand
_why_ you're listening to a specific subset of music. It might not be that
your taste in music suddenly changed, or that you discovered a new genre that
you're incredibly interested in, even though your most listened to genres or
songs changed for a few weeks, or your listening patterns changed for a few
weeks.

A few examples:

I'm Norwegian, and listen to quite a lot of Norwegian music in Norwegian.
Norwegian music is also European music, Scandinavian music, Nordic music,
etc., and as a result I get music from Germany, France, Finland, Iceland,
Sweden, Denmark, among other countries and languages. However, the reason that
I enjoy listening to Norwegian music is because I speak and understand the
nuances in the language perfectly, while this is not the case with any of the
other languages.

In Norway, we have «russefeiring» (russ celebration)
([https://en.wikipedia.org/wiki/Russefeiring](https://en.wikipedia.org/wiki/Russefeiring))
from approximately mid-April to mid-May. In the recent years, many groups of
«russ» have been making/ordering songs to represent them throughout their
celebration, and in that period, I listened to some of those songs because it
was fun during that period (around April-May), but it's not interesting to
listen to outside of that time period at all, pretty much like Christmas
songs. Now it's the second week of October, and my Discover Weekly list still
contains a lot of songs (12 of 30 songs) created specifically for the
«russefeiring». Imagine getting half your Discover Weekly filled with
Christmas songs in May, because that's pretty much my experience with this.

I don't know what they have to do to make Discover Weekly not give me shitty
suggestions, but right now it keeps giving me suggestions that are outdated
and uninteresting and I have no good way to give that feedback.

I'm really saddened by this, because for the first half year of Discover
Weekly, the playlists were so good that I stored them in separate playlists to
be able to go back and listen more to them.

~~~
pj_mukh
I _really_ wish I could get Spotify to "forget" or "reset" my preferences.
When I first started using Spotify, I listened to a lot of slow calming music
(because I was working). Now ALL my Discover weekly's are just 'Iron and
Wine', 'The National' derivatives. Its made Discover Weekly useless to me.

~~~
scrooched_moose
I have the same issue with Iron and Wine and I don't get why. Every
recommendation engine I've tried decides I absolutely love them, no matter
what seed I start with or what songs I like/dislike.

I don't mind Iron and Wine but they are definitely a 'meh' for me, so it gets
frustrating when they dominate every playlist. And, disliking/thumbs down
never seems to get rid of them.

Are they generic enough that the algorithm finds something similar between
them and everything I like?

~~~
n42
You might be on to something. Iron and Wine might be the Kevin Bacon of music
recommendation algorithms.

------
erikbern
I built the foundation of this system while at Spotify. While it's true that
we looked at a lot of different signal, at the point when I left (early 2015),
it was all based on collaborative filtering.

The reason collaborative filtering works so much better than anything else is
that given enough data, it will already encompass everything else. If there
are reasons why certain users prefer certain sounds, or certain lyrics, those
patterns will emerge in the listening data.

The main reason to use any non-CF method is mainly for new content that
Spotify doesn't have much listening data for.

I'm no longer at Spotify, but let me know if you have any questions

~~~
capkutay
I guess this is a less technical question - but what would your advice be to
an artist who wants fans of their more famous influences (similar artist) to
discover their music? Aside from the obvious (tour, market yourself to popular
playlists etc).

Sounds like there's some NLP and web-scraping involved...so would it make
sense to come out with blog posts that compare you to the famous artists that
influence you?

~~~
erikbern
only thing you can do to influence the collaborative filtering system is to
make users listen to both you and some other artist.

as i mentioned in another reply, CF is really what powers DW

~~~
Mz
In this context, what does CF and non-CF mean?

Thanks.

~~~
traek
Collaborative filtering methods vs non-collaborative-filtering recommendation
methods.

~~~
Mz
Thank you.

------
steve-benjamins
As an artist, Discover Weekly has been the best thing to ever happen to me.
Every Monday I get a big infusion of listeners (around 5,000)— many of who
stick around and check out my other music :)

Prior to that, the best press I could get was the tedious process of cold-
emailing bloggers (a practice which is now dying off).

~~~
landtuna
Haha - read your comment, looked at your username, opened Spotify, and
"Purification Ritual" is the first song in my Discover Weekly.

~~~
jcb1973
And now thanks to discussion of Discover Weekly, I've discovered Steve
Benjamins... Thanks!

~~~
jcb1973
And "Hi Steve!" \- listening to "Purification Ritual" now...

~~~
steve-benjamins
Hi! :)

------
aidos
Discover Weekly works really well for me. It surfaces a lot of music I've
forgotten about and for the most part provides an interesting collection to
listen to. Having said that, I have a _very_ eclectic taste in music, so it's
probably harder to hit on things I won't like. My process is to try to listen
to it several times through and then pick out the stand out tracks once
everything has had a chance to grow on me.

One thing I worry about with it though is how much my behaviour might
influence the choices. For example, if there's a track in there that I already
know quite well, because I like it, I'm often scared of skipping it in case
the algorithm takes that as a massive negative signal.

~~~
archagon
In terms of behavior influence, I listened to pretty much exclusively Hamilton
for like a month and didn't get very many new Broadway or hip-hop songs (which
is good for me because I tend to not really listen to either genre). So there
must be significant weight given to songs/genres you've historically listened
to a lot.

~~~
confounded
Or, based on the way the algorithms work, people that listen to the Hamilton
soundtrack a lot, don’t listen to hiphop/broadway a lot.

------
chewz
With all that AI excitement Spotify, Google Play Music and Apple Music
misserably fail at generating good musical recomendations. At least for me.
Spotify Discover Weekly was no different for me when I still used Spotify (six
months ago). Not only it is not personalized at all it is also quite lame. I
am doing enough to seed - following enough artists, genres, liking album,
subscribing to playlists but the quality of recomendations is mediocre at
least.

For example at the moment Apple Music is suggesting to me four Wednesday
playlist - all heave metal the genre I never liked and listened to. Also two
artist spotlights - Jeff Chang and Danny Chan. Yes I have Japanesese account
but this is Cantopop and Taiwanese crooner - so a little off the map
especially that I do not enjoy pop music at all especially Asian. Some other
examples are what I call comin denominators. Yes I follow lots of jazz but
Frank Sinatra is not jazz music, nor Tony Benett. etc...

~~~
smartbit
Same here with Netflix: their recommendations have been a complete failure. It
seems like Netflix is pushing mostly their _Netflix original series_. What
does works for me in Netflix are the _similar to this_ below the movie, e.g.
after you've viewed the movie. But that is a very simple algorithm any
_sophomore_ could write, not related to the recent developments in _neural
nets_ / _machine learning_.

When I go to an filmfestival or an concert, part of what I pay goes to the
proffessionals who make the selection for me. And I'm happy to pay their
services, just like I pay for journalists.

------
a254613e
> Unlike Netflix, though, Spotify doesn’t have those stars with which users
> rate their music. Instead, Spotify’s data is implicit feedback

I wish they kept it simpler with ratings, and explicit instead of implicit.
The discovery weekly playlists are absolutely horrible for me to the point
that I don't even bother checking them nowadays.

What works better for me for discovering new music with spotify is right
clicking on an existing playlist and then "Create Similar Playlist" \- that
gives way more control over what kind of genre/style should the playlist
consist of.

~~~
Systemic33
Explicit and implicit rating systems will never fit everyone.

But I will go as far and say that implicit rating fits a lot more than
explicit, because it doesn't require the user to do extra work on top of the
base goal of listing to "good" music.

I feel like explicit rating scales very poorly with catalogue size. So when
you have music, and as much music as Spotify has, then the work effort of
explicitly rating your taste becomes too big, you start to not bother, and
quality of rating becomes poor.

~~~
jon-wood
There's no reason that I can see not to combine both explicit and implicit
rating. If I really love a song let me mark it as such, but also feed things
that I've chosen to listen to/skip/add to my library into that algorithm
(possibly with a lower weight).

~~~
tormeh
You already can 'save' songs in Spotify. I would hope they take that into
account.

------
cjCamel
For anyone interested in the Raw Audio Models section of that articles, there
are some fun endpoints[1][2] from the Echonest API that provide those models.

They've moved over to the Spotify API since I last had a play, but it's great
that they still provide them.

You can get the audio breakdown of a track, as well as a summary of the track
features including fun stuff like "danceability" and musical positiveness
("valence"). Radiohead's "Fitter, Happier" was low on both of these points if
I remember correctly.

[1]: [https://developer.spotify.com/web-api/get-audio-
features/](https://developer.spotify.com/web-api/get-audio-features/)

[2]: [https://developer.spotify.com/web-api/get-audio-
analysis/](https://developer.spotify.com/web-api/get-audio-analysis/)

~~~
a1k0n
I also wanted to nitpick this part of the article -- the neural network isn't
determining the audio features; these are all hand-engineered features
developed by The Echo Nest.

The neural network is trained to mimic collaborative filtering vectors from
raw audio. It's a separate model from time signature, key, mode, tempo,
loudness, etc.

------
ricardobeat
Discover Weekly works well enough for me - I do wish I could exclude devices
from influencing that playlist as what I listen while gaming on PS4 is
completely different from the rest of the day. It also seems to have a bias
towards “big” commercial releases.

Spotify’s “intelligence” in general is a huge let down though. Radio stations
are extremely limited - more like 15-song static playlists indefinitely on
repeat! It annoys me to no end, same for daily mixes. I end up listening to
the same songs over and over and over and over again. Maybe that’s what makes
them the most money?

Last.fm was amazing at finding me new music I liked. Rdio was amazing at..
radio :D I used to go for months on the same station. I miss both a lot, and
occasionally I still use last.fm or everynoise.com to generate better
playlists for Spotify.

~~~
cr1895
>It also seems to have a bias towards “big” commercial releases.

I haven't noticed this bias at all. It might be due to our own music
preferences.

~~~
ricardobeat
I don't listen to pop very often - that's how I notice it. Eventually I'll
realize that a song it added to my playlist is playing in the background in a
shop, on TV or the radio.

------
erokar
A curious side effect of Discover Weekly is that it sometimes influences what
I listen to, i.e. I'm afraid to listen to a song in a particular genre because
I don't want Disover Weekly to get the wrong idea. But then again, maybe
Discover Weekly knows me better than I do.

~~~
bbx
I had the same issue: I spent a couple of days listening to old tunes I rarely
pick anymore, and it “messed up” my Discover Weekly. I thought “Well I only
have myself to blame, don’t I?”.

Turns out Spotify has a “Private session” mode to prevent influencing your
taste.

------
tjoff
Discover Weekly is weird. For the longest time it kept recommending Finish
music (roughly 30% of all recommendations), I'm from Sweden and barely ever
listen from anything from Finland (or Sweden for that matter).

They have a _ridiculous_ bias towards covers. I bet I've been recommended 50
(I wish that was an exaggeration) version of the Gladiator theme ('now we are
free'). And they are all terribly bad (as in blood coming out of my ears bad).
I am genuinely ashamed that someone thought that it would be a good idea to
submit it to spotify - and if so how spotify could recommend it to anyone,
there is no way spotify could have gotten any indication that anyone has ever
liked any of those versions of that song. Similarly I get lots of game of
throne covers, and now Despacito covers (never listened to that song in any
variant on spotify, on purpose at least).

~~~
HearMeRoar
I’m from Sweden too and I always get at least 2 german/danish songs in my
discover weekly and I hate it.

~~~
KozmoNau7
Entschuldigung/undskyld

------
kolp
Spotify's discovery engine playlists (Discover Weekly and Release Radar) just
don't work for me. I now ignore both playlists and I suspect that the lists
are influenced by payola.

I've tried training the algo by following artists and saving albums in the
style that I would like, but these playlists keep peddling stuff that is way
off the mark. Interestingly, the daily mix playlists have responded to this
training, but not Discover Weekly or Release Radar.

For users who are tired of the same songs being fed to these playlists each
week, you can create an IFTTT action to save the content of these playlists in
separate archive playlists. Once a song is in the archive, it won't (or
shouldn't) appear in either of the weekly so-called discovery playlists.

edit: grammar

~~~
freetonik
> Once a song is in the archive, it won't (or shouldn't) appear in either of
> the weekly so-called discovery playlists.

They still do, sometimes, at least for me. But more importantly, adding songs
to playlists sends the positive signal to Spotify’s recommendation engine, as
far as I know. So, saving all your discover weekly tracks into archive
playlists will encourage Spotify to give you similar music in the future.

------
luckydata
I spent quite a bit of time thinking about the recommendation algorithm and
(after falling into a death metal hole I can't seem to escape on Spotify) I
came to the conclusion that their analysis of the audio content of the song is
way too shallow. You listen to songs because they have a common harmonic
structure, or rythm, but not necessarily the same spectrum. That's why a metal
fan might dig a cover of Metallica by Iron Horse

[https://www.youtube.com/watch?v=3c7bISLhVl8](https://www.youtube.com/watch?v=3c7bISLhVl8)

Collaborative filtering seem to be a much bigger contributor to the
recommendation right now than objective factors about a song itself.

~~~
mulletbum
I just always assumed it would be better to match up peoples likes/dislikes
against one another. For example, I might really dig Brittany Spears, but I
also like Pantera. Considering how those types of music have nothing to do
with each other, it would be much better to match me up against other people
who have similar taste in music as me.

So let's say for example there are 1,000 people who like Spears and Pantera
(specific songs). Now take those 1,000 people and compare the other artists
and songs I am listening to. Let's say, of the 1,000 people who like Spears
and Pantera, 680 listen to a specific song by Creed. That's a high hit rate,
which means that song should be offered to me as I will most likely enjoy it.
It doesn't matter that Creed doesn't sound anything like Pantera or Spears.

~~~
mediocrejoker
I am not a data scientist, but this seems like a very obvious solution to me
as well so I wonder if there is a reason it's not done this way.

~~~
hbosch
You end up in a echo chamber and quickly exhaust your recommendations.

------
eminaz
I use Spotify's Discover Weekly on a regular basis. On average I like more
than half of the songs on the playlist, and it's my main way of discovering
new music.

------
acdha
I find the premise fascinating because of how bad the Discover Weekly
recommendations were when I was looking for a replacement for Rdio. Rdio did a
great job of suggesting new music but with Spotify I found myself constantly
skipping tracks – combined with not having a way to play album tracks in
order[1] I found no justification for using, much less paying for the service.

1\. Yes, they claim to offer that for paid subscribers. As a former paid
subscriber I can conclude only that they either lack a QA department or I
ended up in the A/B bucket from hell.

~~~
smeyer
> combined with not having a way to play album tracks in order

As a paid subscriber for several years, I've had no difficulties playing album
tracks in order. I'm not sure what issue you were having, though.

~~~
acdha
If memory serves there was a button which was supposed to toggle shuffle and
ordered playback. That didn’t work on either iOS or the desktop apps and the
support team took a week to respond with a copy and paste of the instructions
I’d said I was following in my original email.

I’m totally willing to believe that it was some config bug but the effective
lack of support was quite a disappointment, especially with Rdio for
comparison.

------
dontreact
Pretty cool to see them go after the cold start problem with audio models.
Often measuring the impact of having cold start vs. not is very difficult
because it only affects the long tail of tracks at first, but eventually it
could change the dynamics of discovery in the system.

~~~
marcus_holmes
It occurred to me, though, that this is wide open to hackery.

Find a popular song that is likely to be on a lot of people's playlists. Make
a new song that is a close match to it for raw audio modelling. Launch on
Spotify.

Yeah you'll probably only get one or two plays per user, but spread over a
large number of users (who all listen to the whole Discover playlist every
Monday, like I do) it's still significant traffic.

Or have I got this wrong?

~~~
dontreact
I imagine that once you get to any significant number of plays (N=1000, which
would not be a significant payout on spotify at all), the collaborative filter
model would take over and if none of the people who listened to it caused
further positive signals as mentioned (visiting the artist page, repeat
listens) then the track would stop being recommended pretty quickly.

Also, what you are describing sounds like what producers already do because
people actually like it: find the latest trends in sound and copy them, (but
hopefully with a fresh twist so that people get into it) :)

------
nik736
I am still missing Rdio in that regard, they were just playing the right songs
all the time. With Spotify this is absolutely not the case and it's even far
worse than YouTube.

~~~
thirdsun
While I haven't found a recommendation engine that works well enough for me,
rdio's was very close to it.

It's big feature, among others, was their heavy rotation section - I only
followed people that shared a similar taste to mine and we had a really cozy
circle of listeners whose current favorites were surfaced by said heavy
rotation section. We discussed albums in comment sections, shared playlists
and I regularly stumbled upon familiar usernames and friends when discovering
new gems. This social component to discovery is completely missing from
Spotify, yet it's more powerful than any recommendation engine I have used,
including Spotify's attempts, which I'd rate as mediocre.

I miss rdio.

------
piahoo
I wish I could feed Spotify ML algorithm with my last.fm play history (100 000
songs) or rateyourmusic albums (3000 rated). It would be much more accurate
(probably about 3000 plays from Spotify itself)

------
mskullcap
Rdio worked the best because I could befriend people with similar tastes. I
don't understand why this social aspect isn't being used explicitly on
Spotify. Rdio knew I had some eclectic musical tastes and yet could find me
other people with similar tastes - I found my best music by browsing through
other people's collections of music.

------
bubblethink
Off topic: Does anyone remember thesixtyone ? I loved that site, and a lot of
the music that I listen to was discovered there. It has a very "the day the
music died" sort of feeling for me, when they shut. They were healthy until
2010. Then it shut for while, was on life support for a while, and was
officially killed sometime last year. It was very simple and light, and had
interesting music. I never found (or put too much effort honestly) to find a
good replacement among the next wave of music websites. I can't stand the
whole log in with facebook and enable drm in chrome to listen to this song
thing. Just want some new interesting, indie music. Suggestions welcome.

Further off topic: Among the commercial providers, I found this Indian music
site very refreshing ([https://gaana.com/](https://gaana.com/)). No sign-in,
no flash, no widevine.

~~~
zohaibr
Yes, I remember thesixtyone. You're right it was a great discovery tool for
indie music. I just browse reddit.com/r/listentothis mostly on the comments to
find something unique and interesting music.

------
yilugurlu
They're not anywhere near as last.fm's recommendations.

~~~
DanielleMolloy
In my experience they got pretty close to Pandora's recommendations already,
which were excellent.

last.fm for me just recommended the next big artist in the same category.
Often their music styles were still obviously different and the recommendation
rather put me off. People that are hooked by the complexity, details and
perfectionism in "Nightfall in Middle-Earth" won't necessarily like Manowar.

I recently got one Spotify recommendation that lead me into listening through
a band's full catalogue and getting tickets for their show two weeks later
(Insomnium, btw). It also dug out a song that I liked in primary school but
completely forgot about. They discovered that I like cheezy metal covers of
80s pop songs and add one by some obscure band to my list every now and then.
I'd say my experience was often pretty accurate.

~~~
cyxxon
Thanks, that is one of my main gripes with most recommendation engines, and
since you like metal, the examples really resonated with me. For me, it was
Wardruna - an ambienty, folky acoustic group. Listeners often also like the
"typical" viking metal bands, but damn, if I want something like Wardruna, I
do not want death metal singing about the same topics, I want, say, Forndom.
The same often happened in electronic music as well: if I listened to 5
relatively unknown psytrance songs I liked because they have a nice balance of
melodies layered with very distinct synth sounds, then no, I don't want to
liten to David Guetta next, because that is also trance-like.

In other words: yes, last.fm's recommendation was much too much based on
"customers also liked", which helps in a lot of cases, but so often it
horribly fails ("Customers who bought The Martian on Blu-Ray also bought this
asthma medication because chance happens. Wanna try it out?").

------
jmuguy
My problem with discover weekly is I basically listen to boring background
music like vaporwave all day at work. So while I like a large variety of music
from all sorts of genres, since I spent 8 hours a day listening at work, my
discover weekly is just slammed full of that stuff.

~~~
sequence7
You could turn on Private Listening[0] at work, that way the boring background
music won't affect your suggestions.

[0]
[https://support.spotify.com/us/using_spotify/the_basics/how-...](https://support.spotify.com/us/using_spotify/the_basics/how-
to-enable-private-listening/)

------
pdeuchler
My Foolproof Guide To Managing Your Discover Weekly Playlist:

\- Only listen to the DW playlist once through. Find the songs you even
remotely like, put them in a new playlist. Listen to that playlist instead (I
call these playlists "DW-{datestamp}").

\- Find your favorite songs in the playlist, explore that artist/album. Even
if you don't think you'd like the other stuff listening to more of an artist
seems to help suggestion variety.

\- Don't let your listening for the week be dominated by a good Discover
Weekly playlist... every time I do this my next 2-3 weeks are total crap. If
you must repeat the same songs over and over, move them to a new playlist.

\- Try to mix up genres as much as possible... listening to different genres
that aren't your favorite often leads to Spotify recommending off the wall
artists in the genres that are your favorite.

\- Keep a playlist of your most frequently listened songs, regardless of
genre, artist, etc. Whenever you want to listen to one of favorites listen
from that playlist, instead of going to their artist page. For some reason
this seems to have a large effect on my recommendations.

Most of these suggestions are due to personal experience and two theories:

1) Grouping/organization/total play count of playlists influences
recommendations much more than people think

2) It's very easy for Spotify to get into positive feedback loops, forcing
variety and constantly curating/making new playlists expands your horizons and
keeps the recommendation engine from repeating songs/artists too much.

There seems to be a common misconception, even among programmers who should
know better, that Spotify will just instantly and always offer you fresh,
undiscovered, music you like on demand... which isn't how these systems work.
It definitely makes it easier to find new music, and is a valuable tool in
finding new artists or under marketed artists, but you're still going to need
to put in a modicum of work curating your own experience to get the most out
of the discover playlists.

~~~
bayonetz
Feels like an SEO hacker trying to understand and influence a black box search
result ranker like Google's. Which is kind of nuts and you'll always be a step
behind. Would be pretty cool and interesting if a recommender like Spotify
would publish a guide on how to get the most out of their system. Like what
you did, but from the builders themselves.

------
dotdi
I discovered how good Spotify's Discover Weekly was just a few days ago.
Basically, I liked all but 2 songs on that list.

My taste in music is also rather specific, which made it even more impressive.
I shall see how the following weeks fare.

~~~
flohofwoe
It's nice the first few times, but after a while I get the impression that I'm
trapped in a "Groundhog Day" loop and hear the same music over and over again.

Spotify should add a slider that lets me widen or narrow the 'search area',
sometimes I want to hear more similar music, sometimes I want to find more
stuff at the edges where all the interesting stuff lurks.

~~~
KozmoNau7
Spotify seems to mostly stick with the ~5 or so most popular song by a
particular artist, so I am seeing some repetition. I don't mind, since I like
most of those songs, but you should really take them as artist
recommendations, and explore their other songs.

I've found, that if you put in more effort into discovering music yourself,
Spotify's recommendations improve.

------
unethical_ban
I took a genetic algorithms course in college. I was undergrad and the prof
needed to "make" the grad course. I recall the idea that when writing these
algos, you had to inject mutations into your adjustments, in order not to hit
a local maximum.

I think the same of Spotify. Several comments here discuss how it gets too
focused, or you're unable to reset preferences, etc. I too wish I could
"reset" my daily mixes, or else change it up a bit. Wouldn't even be great if
you could have some sort of advanced option to adjust the "genre variability"
of your stations?

------
mkane848
I'm going to have to go ahead and plug SAGE[0], because between Spotify
recommending me the same bands/songs ad nauseum (or just being way off),
friends already telling me they used it to find a new band to listen to on the
first day it launched, and just being a fan of his work in general, it's a
complete labor of love and I think it shows.

[0] [https://medium.com/@hate5six/sage-an-artificially-
intelligen...](https://medium.com/@hate5six/sage-an-artificially-intelligent-
band-recommender-d36a78d94109)

~~~
nineumbrellas
Never thought I'd see hate5six on HN. Nice.

------
delegate
The thing with any 'recommendation' system like Discover Weekly is that while
it recommends based on past preferences, the recommendations have the effect
of influencing and reinforcing the musical tastes and at some point one will
notice that one's taste in music has been entirely manufactured by the
algorithm, week by week.

This is not just in music, the filtering 'according to preferences' is
ubiquitous in today's applications - so I wonder - were does the
recommendation end and influence start ?

For example, Google maps routes you to avoid high traffic, but by doing this,
it is also _generating_ traffic and the more people use it, the more influence
the app has in the real world traffic.

I for one use it sporadically; my music tastes are so state-dependent -
sometimes I want ambient music, sometimes I want heavy metal, sometimes I want
lyrics and sometimes I want a hard electronic beat. The algorithm does not
know my current state, wether I want to keep or change it - even I don't
always understand exactly what and how I feel.

Also, I've had it happen lots of times - sometimes I listen to a track or
album which I don't immediately like, but then it grows on me and I discover
something beautiful hidden in it. There's value in listening to things that
don't follow the usual pattern and that's very hard for an algorithm to do.

~~~
ethbro
Did you read the article?

Only 1 (audio analysis) of the 3 models (collaborative, nlp sentiment, audio)
doesn't mix in recommendations from non-you sources, thereby surfacing new
music to your attention.

It explains why I tend to like Discover too. Precisely because it doesn't
duplicate my exact tastes.

------
spike021
I've been using Apple Music the past 8 or so months because somehow while I
was finishing up school I ended up on their student discount plan and not
Spotify's.

I really miss Spotify's Discover Weekly. Apple Music (AM) has a similar
feature, called "New Music Mix", but it's never as accurate.

While I was still using Spotify heavily last year, almost every week I'd
duplicate the Discover Weekly playlist so I could keep that exact mix because
a majority of the songs would really fit my current music tastes. Nowadays
with AM I only duplicate a New Music Mix playlist once every month, if that.
It's ridiculous how uninspired the playlist from Apple is and oftentimes it
includes music that isn't similar to anything I listen to.

Discover Weekly is the one feature that would bring me back to Spotify and let
me ditch AM once my discount period expires.

------
confounded
Isn’t this article conjecture written by an enthusiastic fan?

Do we know anything about the algorithms actually used?

------
anon1253
It's very impressive. But, I'm curious about some of the technical details.
For each of these representations you basically end up with a dense vector on
a song level. Which I assume you would then kNN with a user specific vector.
But I've never come across a nice kNN data structure that supports high
dimensional vectors in a larger than memory setting whilst supporting updates.
Spotifys own Annoy is cool
[https://github.com/spotify/annoy](https://github.com/spotify/annoy), but
changing or adding a song requires rebuilding the whole structure ... surely
that's prohibitive at scale?

~~~
erikbern
Spotify re-runs the latent vector models regularly and re-indexes them into
Annoy indexes. There is no need to do that in real time, you can be a few
weeks delayed and it's usually fine. New music doesn't have much data and need
different methods anyway.

------
reitanqild
I read a few threads down but couldn't find it so here it goes:

I'm quite unimpressed by this feature. It knows full well that I almost only
listen to music without lyrics (trance genre to be specific) when I work.

Sometimes I try the automatic playlists including discover weekly and what do
they play? 50% vocal.

I skip as soon as I recognise it but they never learn. I also mostly play from
a precompiled list containing only instrumental trance but even that is not
enough.

At least they are in good company. Google has seen all my searches, my photos
and my mail since before I met my wife and yet for years they figured out the
most relevant ads they could show me was for dating sites :-/

------
hashhar
They still suck compared to what Last.fm recommends me. Also the fact that
Spotify uses implicit feedback instead of explicit like Last.fm pisses me off.
I'm constantly left asking what the algo would make of my actions.

------
Dowwie
Here's something I would never be recommended. I discovered the group by
chance when listening to NPR one day -- The Frightnrs:
[https://www.youtube.com/watch?v=gwibcfkfuGQ&list=RDEMedGoIEt...](https://www.youtube.com/watch?v=gwibcfkfuGQ&list=RDEMedGoIEtFJbdf2S85opSTdQ&index=5)

It's really unfortunate that their lead singer died of ALS as the group
completed producing its album.

This recommendation was based on the preference of a third party (the DJ). I
discovered by sharing his listening preferences.

------
lolive
Personnaly, I still use the good old "This Weeks Releases" playlist managed by
user jlarome.

[https://www.google.fr/url?sa=t&rct=j&q=&esrc=s&source=web&cd...](https://www.google.fr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjq_dK6k-jWAhVnSZoKHaMPAQwQFggpMAA&url=https%3A%2F%2Fopen.spotify.com%2Fuser%2Fjlarome%2Fplaylist%2F6KErOvRQFEAnY5SdrrBDOD&usg=AOvVaw2QEUMCAMS_nLfkovndCdd9)

------
factsaresacred
I adore Discover Weekly. No lesser adjective will do.

Now turn the data into a dating/social app so users can discover the cools
that share their exceedingly good taste in music.

------
seanwilson
Does anyone know how they evaluate how successful their new recommendation
algorithms are? How much do they improve on simpler algorithms?

It's not obvious/intuitive the NLP and audio model algorithms mentioned would
be that successful. I would have thought collaborative filtering + showing you
new tracks from artists you like + showing you new tracks in genres you like
would get you most of the way there.

~~~
nkozyra
I really don't understand why collaborative filtering isn't the sole approach.
It's basically exactly what you want if you have that volume of data.

~~~
anjc
Relying on collaborative filtering (using play counts, as Spotify do) can lead
to non-diverse and non-serendipitous recommendation. If I like Abbey Road then
a typical collaborative filtering scheme might tell me I like Sgt Pepper's and
Let it Be. Excellent recommendation, but useless if I'm already familiar with
the band and the genre.

There might be an unknown Japanese band that'd suit my tastes perfectly and
I'd never know with Collab filtering.

~~~
nkozyra
That just seems like a narrow net problem than an issue with the approach.
Assuming I have some distance function that tells me who's "similar" to me and
it's malleable, I should be able to reach those outliers.

~~~
anjc
Yeah, makes sense, and for sure works for many cases. But there's no
meaningful distance between two sets of preferences that have a null
intersection.

I've come across this just as a user with regard to film. I reckon that I've
watched all of the 10/10 mainstream comedies that have been made in English. I
want more 10/10 comedies, so I'll have to look at foreign languages films. But
there isn't enough overlap of user preference, due to demographic separation I
suppose, to get decent recommendation from other countries. If you start
adding metainformation preferences (e.g. must not be in English, must be >9/10
stars etc) then you're back to Content-based recommendation territory (i.e.
audio analysis as Spotify are doing)

------
beepboopbeep
Discover weekly is usually just generic garbage. The daily mixes aren't much
better, usually playing the same songs over and over in a different order.

I have a ton of songs that I've found over time that I like that spotify has
managed to kill for me.

I really enjoyed soundcloud's music and tended to find way more interesting
stuff on there. Too bad it's apparently run by morons.

------
jaideepsingh
One thing I want in Spotify recommendation engine is proper multilingual
support. I have a varied/eclectic taste in English/Hindi/Punjabi music that
spans decades and there are many sub-sub-genres that Discover Weekly doesn't
capture. It keeps recommending me Telugu/Tamil song, probably based on beats,
but I cannot understand them haha

------
kspy
I find that with spotify I get a more consistent variety of music. However I
actually pay and use google music primarily. Wish there was a feature on
google music (besides feeling lucky or radio) that had curated content like
spotify does. I'm usually able to find at least a song I like that comes up in
the discover weekly

------
GreenPlastic
My problem with Discover Weekly is it weights recent listens way too heavily.
I was listening to R&B for awhile and really loved the new songs it was
pulling up - then I switched to jpop and then the list was entirely jpop. Over
time, if you don't listen to other music it becomes kind of eclectic /
coffeeshop pop.

------
Reith
Weekly suggestions is pretty good for me but daily suggestions can be horrible
for less widespread listening. Yesterday I listened some Iranian traditional
music for first time with Spotify and It didn't take much time to suggest
based on traditional and theosophical music, some dirty hip hop music full of
swear words.

------
jasonrhaas
I think that some people don't like the Spotify Discover Weekly because they
don't want to admit that they like crappy music. Its kind of like when you
hear your voice on a recording, you're like... "do I really sound like that?"

Just face it, the algorithm has you pegged.

~~~
e40
For the first few months, I really liked DW. The last few weeks, it's been
giving me almost exclusively country rock. That is very different than it was
before, though I would like an occasional country rock song (The Outlaws, for
example).

I'm kinda bummed by this, since I really looked forward to listening to it on
Monday going to work.

------
vasilakisfil
From my personal experience Spotify has really bad recommendations, even
YouTube has better ones.

------
tuxguy
[https://labs.spotify.com/2017/06/07/meet-our-engineers-
charl...](https://labs.spotify.com/2017/06/07/meet-our-engineers-charlie-
pastuszenski/)

------
partycoder
Other Spotify features include: your daily mix (like discover weekly, but
daily, and providing an individual playlist per each genre), release radar,
charts, suggesting songs given a playlist, and creating a radio from a song.

------
fori1to10
Google Music's "I'm feeling lucky" is awesome. This is very subjective, but I
feel it makes much better predictions as to what I'll like than Spotify. Maybe
someone else has a different experience?

~~~
mcjiggerlog
Google Music was, in my opinion, the industry's greatest music discovery
service back when it used to have the "Explore" tab where you could drill down
through genres and sub-genres to find popular or classic albums within that
genre. It also used to give me much better recommendations on the homepage for
new albums that I might like.

It's all gone down hill since they switched everything to mood-based radios. I
feel like the main problem is that I like to listen to albums and Google seems
to assume I only want to listen to random streams of disconnected singles. I
seem to just get played the same stuff over and over, too.

I don't know how to find new (new to me, not the world) music anymore. Are
there any good services that anyone recommends?

~~~
1_player
> I don't know how to find new (new to me, not the world) music anymore. Are
> there any good services that anyone recommends?

I have the same problem. Usually I find new stuff on the various music
subreddits and last.fm's Similar Artists page. Even though I have tons of
playlists and saved songs on Spotify, their recommendations usually suck
(probably because I listen to just about every music genre under the sun.)

Their radio feature rehashes the same songs I've heard dozens of times, and
Discover Weekly is usually so awful I never listen to it.

------
zitterbewegung
I really like the performance of Discovery Weekly. At least half of the songs
are songs I really like. I have frequently saved songs on discover weekly. I
love Spotify recommendation system also and daily mixes.

------
visarga
As someone who doesn't have access to Spotify, I think Youtube should put more
effort into recommendations. They have the same tendency to lock onto
something too hard.

------
antibland
I truly wish Spotify had a "weirdness level" slider. Mine would be dragged to
the max, as I find this service generally provides pretty tame choices.

------
peteretep
Has anyone found anything for books that doesn't _completely_ suck? Amazon,
LibraryThing, GoodReads, the recommendations are all appalling.

------
faustocarva
I still prefer the human radios like KEXP, Radio Paradise and NPR All songs
considered. Nothing is better than a human radio programming.

------
herf
Collaborative filtering was done at the MIT media lab, and commercialized by
Firefly in 1995 (way before last.fm)

------
barrystaes
If only Spotify would learn i dislike autotune, and focus less on the
artist/genre/year of music.

------
zkomp
I wish there was a way to turn this feature completely off. I used to like
Spotify but lately the experience has been constant bugs and to be frank,
insultingly bad recomendations... stop this nonsense!

Every time I open Spotify this crap shown on the top, before my own lists, it
makes me angry every time.

------
Gargoyle
What I want to know is why is Spotify's desktop app so appallingly awful? It's
by far the worst user experience I have on Windows.

------
madshiva
They don't find new music, but music that other already know, then the music
is not new but the trend music. It's like recommanding despacito.. all these
waste of ressource for acheiveing this is amazing.

~~~
RationPhantoms
What you're saying flies in the face of modern findings of the power of "crowd
knowledge" and is actually wrong. I, amongst others, find the feature
incredibly useful so maybe it's a waste of resources on just you.

------
jerianasmith
I would love to see if Spotify could take a leaf out of Google Play music.

------
criley2
Machine learning, lol. Ok.

I can write a Discover weekly in about two lines of psuedocode.

IF user.history.containsEvenOne("rap/r&b") THEN
discover.weekly.setAll("rap/r&b")

TA-DA~ you now have the complete "Discover Weekly" experience.

~~~
madshiva
you have forget the badge!

------
konschubert
Can't they just make great recommendations based on:

People who have music X on their playlist also have Y a lot. Person A listens
to X but not Y. Let's make them discover Y.

You could just build a topology of songs like that and then recommend songs to
user A if they are topologically close to the songs he likes.

EDIT: Read the article now :D they do that and it's called Collaborative
Filtering.

~~~
icebraining
It's also how Criticker recommends films, and I have to say their predicted
scores are uncanny. After watching a film, I often think of a 1-100 score and
then look it up on their site, and it's at most ±2 from it.

(Not affiliate, just a big fan)

~~~
rom16384
Another movie recommendation website is MovieLens,
[http://movielens.umn.edu/](http://movielens.umn.edu/) which is run by a
research group of University of Minnesota.

