4 ms·

How cool would it be to have a recommendation system for music that follows mood? That could be trained on actual music not by me saying "I listen to songs x,y,

by jack_pp 10y ago

How cool would it be to have a recommendation system for music that follows mood? That could be trained on actual music not by me saying "I listen to songs x,y,z when sad, a1,b1,c2 when it's sunny and i'm feeling good etc". If this will ever exist it would probably increase my hedonic index 10 fold

xapata 10y ago
Songza, acquired by and merged into Google Music, is trying to do that. I've recently converted from Spotify.
jc4p 10y ago
I've been working on something similar for a long time. You can get far by using Last.fm to track your own music listening history and analyzing against notes (break-ups, rainy days, etc).
halflings 10y ago
There's certainly a lot of research trying to incorporate things like this into recommendations, but it never really made it to mature products (apart from some experiments on last.fm, 8tracks, etc.) Spotify tries to accommodate this by offering curated playlists based on moods, but knowing a user's mood to make that type of recommendations is hard. (it's a bit intrusive for a music streaming service to ask you how you feel every time you start it up)
- vidarh 10y ago
  If I could start a service, and trust a skip to indicate "no, you got it wrong, try something closer to the last track I didn't skip", it could often narrow in fairly quickly. Perhaps offer a few more alternatives for skipping that'd indicate reason. E.g. "want something more up-tempo/downtempo/more cheerful/sadder".
  - Drdrdrq 10y ago
    Assuming you know what you want of course. But the recommender system could have a mode where it learns which taste you like based on time, number of unread mails in your inbox, skipped songs and moon cycle. :)
    - vidarh 10y ago
      But the point is you don't need to know what you want. You only need to think you do. You can let the system adjust what the buttons means too, based on your subsequent behaviour.
amelius 10y ago
Perhaps this could be researched by using a meditation headband (brain sensor), like http://www.choosemuse.com/ http://www.choosemuse.com/
vidarh 10y ago
I keep telling people that music recommendation algorithms needs a lot more data. Weather very much could play into it, but also time of day, and location. As well as what I let play to completion before and after a track. I listen to very different music during working hours, during a commute, and near bed time, for example.
flashm 10y ago
I've always thought that music recommendation systems should look at the actual music, and not as much at the surrounding meta-data. What kind of instruments are used? What key is it in? What structure is the song in? i.e does it have a standard format, is it prog, is it a symphony etc What language is it in? What Rhythms are used? That information, used properly, should be able to actually recommend music that the listener enjoys, not just guesswork that is usually rubbish. The Spotify algorithm for example...

by jack_pp 10y ago

xapata 10y ago

Songza, acquired by and merged into Google Music, is trying to do that. I've recently converted from Spotify.

jc4p 10y ago

I've been working on something similar for a long time. You can get far by using Last.fm to track your own music listening history and analyzing against notes (break-ups, rainy days, etc).

halflings 10y ago

There's certainly a lot of research trying to incorporate things like this into recommendations, but it never really made it to mature products (apart from some experiments on last.fm, 8tracks, etc.) Spotify tries to accommodate this by offering curated playlists based on moods, but knowing a user's mood to make that type of recommendations is hard. (it's a bit intrusive for a music streaming service to ask you how you feel every time you start it up)

vidarh 10y ago
If I could start a service, and trust a skip to indicate "no, you got it wrong, try something closer to the last track I didn't skip", it could often narrow in fairly quickly. Perhaps offer a few more alternatives for skipping that'd indicate reason. E.g. "want something more up-tempo/downtempo/more cheerful/sadder".
- Drdrdrq 10y ago
  Assuming you know what you want of course. But the recommender system could have a mode where it learns which taste you like based on time, number of unread mails in your inbox, skipped songs and moon cycle. :)
  - vidarh 10y ago
    But the point is you don't need to know what you want. You only need to think you do. You can let the system adjust what the buttons means too, based on your subsequent behaviour.

amelius 10y ago

Perhaps this could be researched by using a meditation headband (brain sensor), like http://www.choosemuse.com/ http://www.choosemuse.com/

vidarh 10y ago

I keep telling people that music recommendation algorithms needs a lot more data. Weather very much could play into it, but also time of day, and location. As well as what I let play to completion before and after a track. I listen to very different music during working hours, during a commute, and near bed time, for example.

flashm 10y ago

I've always thought that music recommendation systems should look at the actual music, and not as much at the surrounding meta-data. What kind of instruments are used? What key is it in? What structure is the song in? i.e does it have a standard format, is it prog, is it a symphony etc What language is it in? What Rhythms are used? That information, used properly, should be able to actually recommend music that the listener enjoys, not just guesswork that is usually rubbish. The Spotify algorithm for example...