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Spotify’s Discover Weekly: How machine learning finds new music

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Re: Spotify’s Discover Weekly: How machine learning finds new music

#11
post #9

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.

Opposite experience here: Spotify is absolutely perfect. They have conditioned me over the past 7 years to love whatever they recommend.

YouTube has good automatic playlists but the play-next feature often wanders way off after a few songs

Re: Spotify’s Discover Weekly: How machine learning finds new music

#12
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.

Re: Spotify’s Discover Weekly: How machine learning finds new music

#13
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/

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

Re: Spotify’s Discover Weekly: How machine learning finds new music

#14
post #12

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. O…

Same. Also, anytime I get a bad recommendation I blame my daughter for wanting to listen to the Frozen soundtrack.

Re: Spotify’s Discover Weekly: How machine learning finds new music

#16
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

Re: Spotify’s Discover Weekly: How machine learning finds new music

#17

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

I disagree, Last.fm's recommendations have always felt lukewarm to me. Discover Weekly, on the other hand, has introduced me to TONS of new music that I still regularly listen to.

Re: Spotify’s Discover Weekly: How machine learning finds new music

#18
post #12

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. O…

Wrt fear of sending signals, I feel the same.

Also, I have a young daughter who often asks me to play music in the car or Google Home and those affect my recommendations. I wish Spotify had some switch I could turn on to temporarily ignore anything I did until I switched it back off.

Re: Spotify’s Discover Weekly: How machine learning finds new music

#19
post #12

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. O…

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.

Re: Spotify’s Discover Weekly: How machine learning finds new music

#20

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.

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)

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