Spotify’s Discover Weekly: How machine learning finds new music • Hacker News Board

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

#81

Post by [deleted] » Wed, Oct 11, 2017, 10:28 AM UTC

KozmoNau7 wrote: post #59

iddqd wrote: post #58

Earlier quoted context omitted.

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 leas…

You should branch out from only listening to "Discover Weekly". Use the daily mixes, make your own playlists.

[deleted]

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

#82

Post by mcjiggerlog » Wed, Oct 11, 2017, 10:41 AM UTC

fori1to10 wrote: post #4

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?

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?

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

#83

Post by dom96 » Wed, Oct 11, 2017, 10:42 AM UTC

arkadiytehgraet wrote: post #80

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…

I have the same experience. Although certainly it seems that my mood affects the kind of music that I enjoy. There were times when I listened to my discover weekly playlist on Monday, didn't like any of the songs, then listened again 6 days later and liked the vast majority.

This is difficult to measure of course, and is purely anecdotal. I am also wondering whether there is simply no more music out there that I enjoy, my music taste seems very obscure.

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

#84

Post by StavrosK » Wed, Oct 11, 2017, 10:45 AM UTC

arkadiytehgraet wrote: post #80

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…

This is my experience as well. However, the first song I ever saw on Discover Weekly was this amazing gem, so now I kind of give Discover Weekly a pass for the rest of its behaviour: https://www.youtube.com/watch?v=f2cGxy-ZHIs

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

#85

Post by seanwilson » Wed, Oct 11, 2017, 10:45 AM UTC

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.

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

#86

Post by StavrosK » Wed, Oct 11, 2017, 10:46 AM UTC

tomaskafka wrote: post #63

Asdfbla wrote: post #29

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.

You (and Spotify's team) would like to read this paper; I wish eveyone making recommender systems would. Recommender Systems for Self-Actualization https://dl.acm.org/citation.cfm?id=2959189

Readable link: https://sci-hub.cc/https://dl.acm.org/citation.cfm?id=295918...

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

#87

Post by mattmanser » Wed, Oct 11, 2017, 10:48 AM UTC

arkadiytehgraet wrote: post #80

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…

I don't know, I have friends who listen to some music I like, but lots of music I don't. If it's trying to recommend by that, it's going to miss a lot. And even within my own listening, there's plenty of bands where I like one song, but hate the rest of the album. There's bands where I'm massive fans of certain albums, but only like one song on others.

Music's a weird one. And taste changes. Last night I was listening to Jazz, I usually hate Jazz. Should it start recommending that to me now?

I can imagine what works brilliantly for some people will be abysmal for others (but I guess a decently advanced ML system should detect that and try different approaches for different people).

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

#88

Post by jqbx_jason » Wed, Oct 11, 2017, 11:11 AM UTC

Asdfbla wrote: post #29

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.

If you're looking for more noise to get you out of your music comfort zone then check out JQBX (https://www.jqbx.fm). It's a social music app that let's you DJ and listen to music with others in virtual rooms (similar to turntable). It plays all audio through Spotify so you get a huge library, can save tracks for later, and the random plays can seed your new discover weekly playlists (or you can use private mode so it won't).

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

#89

Post by steve-benjamins » Wed, Oct 11, 2017, 11:14 AM UTC

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).

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

#90

Post by ikornaselur » Wed, Oct 11, 2017, 11:15 AM UTC

arkadiytehgraet wrote: post #80

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…

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.