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)

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".

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. :)

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.

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

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

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).

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...

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.