Finding Similar Music Using Matrix Factorization
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Finding Similar Music Using Matrix Factorization
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Re: Finding Similar Music Using Matrix Factorization
#2Oh, I thought this was talking about looking through the actual notes of the music to find patterns
Re: Finding Similar Music Using Matrix Factorization
#3Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre.
Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point)
This method sort of works, but it mainly yields music I find 'acceptable' not music I find good.
Music should IMO be recommended using genre/tags(artists, year, ...) as well as:
(rather long list:) tempo, complexity of the music, instruments, amount of instruments, how monotone or varied to music is, then there is music that uses notes to keep you in short suspense and others in long suspense, general sound of the music (rock would be 'rough' while violin would generally be 'smooth'), music patterns, music pattern genre, ...
And then you need to train a small neural net per person to figure out witch of all these features is important to the person you are recommending music to.
(Edit: added to list of features to look for in music)
Re: Finding Similar Music Using Matrix Factorization
#4Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre. Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point) This method sort of works, but i…
I think that for me the approach in TFA would work, if it was applied to songs rather than artists. There are many artists that have a couple of songs I like and a lot of ones I don't care for. Artist-based recommendations go terribly wrong for me for this reason, I think.
Re: Finding Similar Music Using Matrix Factorization
#5Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre. Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point) This method sort of works, but i…
Add vocal to this list:
There is a lot of great music I don't listen to because it comes with the performers problems, complaints, and political beliefs attached.
Some people don't care but for those of us who do it would be great if there was a filter for spoken words.
Re: Finding Similar Music Using Matrix Factorization
#6Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre. Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point) This method sort of works, but i…
While it's common to use things like genre, year, musical characteristics, etc, social dimensions may be at least as valuable. Exanples are artists performeing at the same show, collaborating on a project, maintaining personal friendships, signing to the same record label, moving to another group, etc. Most of the music I've found and enjoyed were through these types of connections.
Re: Finding Similar Music Using Matrix Factorization
#7Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre. Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point) This method sort of works, but i…
I think Spotify's "Discover Weekly" playlists get pretty close to this. I really, really look forward to each week's new playlist. I listen to a wide range of genres, and completely agree with you that most discovery tools try to pigeon hole you into one or two. Spotify seems to pick up on other associations. I wonder how much neural net stuff they're doing with their big data.
Re: Finding Similar Music Using Matrix Factorization
#8Cool stuff, did you look at using Latent Dirichlet Allocation to perform the categorization?
Re: Finding Similar Music Using Matrix Factorization
#9Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre. Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point) This method sort of works, but i…
What you're describing is is similar how Allmusic categorises artists (categorised? I haven't used it a lot in the last few years, so I don't know how much it's changed). I used to find most of my new music via it. It was in no way algorithmic - it didn't make suggestions, it was just a case of manually following links (given that part of the pleasure comes from searching and discovering independently, no bad thing). It had genres, mood, influences, influencees, etc at the artist level at least. It would seem that Rovi's API could be better leveraged to provide good suggestions than it currently is, if all that metadata is still being attached (though there are things like Spotify's Discover Weekly that I've found to be surprisingly good which I assume use it).
[It would have been even better if that had been expanded to album/song level. As the sibling comment suggests, categorising by subject matter would be very useful for a not-insignificant number of people as well.]
Re: Finding Similar Music Using Matrix Factorization
#10Personally to me, these methods of finding music don't work. I believe it's because most people tend to listen to music in a genre. Because the above method doesn't work, I have done correlations between different artists I like and the music recommended for that artist. (Example Arists A: Similar B C D; Artists E: Similar C D, then it would recommend C=4 point, D=4 points, B=2 point) This method sort of works, but i…
I think Spotify's "Discover Weekly" playlists get pretty close to this. I really, really look forward to each week's new playlist. I listen to a wide range of genres, and completely agree with you that most discovery tools try to pigeon hole you into one or two. Spotify seems to pick up on other associations. I wonder how much neural net stuff they're doing with their big data.
I was going to say the same, Discover Weekly frequently really impresses me with some of its selections and I have discovered some excellent artists through it. I'm into electronic music - not what I would consider really obscure stuff, but certainly not mainstream - and many other recommendation systems seem top lump anything electronic together with mainstream "EDM" etc. (which is nothing like what I enjoy) whereas Discover Weekly makes intelligent recommendations of quite obscure stuff.
I assume it uses some combination of social data (e.g. music listened to by people who listen to similar artists as you) and the intelligent classification ability they purchased when acquiring Echo Nest. There was quite a good write up about it - I think it was one of these two: http://www.theverge.com/2015/9/30/9416579/spotify-discover-w... or http://qz.com/571007/the-magic-that-makes-spotifys-discover-...
I just wish it would save my previous weeks' playlists as sometimes I forget to listen to them and then they're gone!