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Instead of playing just the tracks in your playlist, why not let your mp3 player know about *all* of your available music, rank it according to preference, and only play the top (for example) 20% of tracks 80% of the time? This could easily be done using a nonuniform random number generator to select
the next track, it seems... Of course, you'd want different scorefiles for listening to different kinds of music, as well. Eventually, after a period of use, one might even be able to learn what tracks "go together", and use that knowledge for generating new scorefiles.
Launch.com
http://www.launch.com
[bookworm, Aug 16 2000, last modified Oct 04 2004]
NetPerceptions
http://www.netperceptions.com
Analysis software that could do this [vegemite, Aug 16 2000, last modified Oct 04 2004]
Cymbaline
http://freshmeat.net/projects/cymbaline/
This MP3 player does just what you describe. [PurpleBob, Aug 16 2000, last modified Oct 04 2004]
GJay
http://gjay.sourceforge.net
Makes playlists from song "color," freq. and BPM [yarnball, Sep 12 2002, last modified Oct 04 2004]
Last FM
http://www.last.fm/
Free streaming radio that learns what you like and suggests songs [nelso, Oct 04 2004]
[link]
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If you were using only top 20% of files, wouldn't the top songs keep raising their rating, and disable others from playing completely? |
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Launch.com does something like this, although it adds an annoying GroupLens-like effect as well, and only has more popular stuff available. |
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I had a very similar idea
earlier when working on my AI
research (top secret and hush
hush so no asking!) and my idea
was this: |
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If you select a particular song
to be played it gets 1
"preference point" so to
speak.. and this is used to
determine the songs to be
played. You can select playing
songs from the top OR bottom
percent, point range, song
count, etc. |
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Would you not then be in danger of homogenising your playlist? Select a few tracks that you like and then the available playlist will be based on those, and future iteratins of the process will simply extrapolate those choices -- you'd end up with a playlist of all the same kind of stuff; thus eliminating any possibility of randomness and of experiencing different forms of music. Much like the popular music charts, really... |
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It could randomly play a less
preferred song, or it might
have a playlist template (like
a real radio station) that
defines ranges of
preference--so the player plays
three tracks you like, one you
don't like so much, two more
you like, and one that's OK but
you haven't heard in forever. |
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It could also note which songs
have played recently and
temporarily reduce their
preference score so as to not
play them again too soon. Those
temporary preference scores
might not be reset until all
tracks have played, so the
popular tracks' scores
gradually drift lower. |
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And of course you should still
have the option of jumping
around your playlist willy-nilly. |
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I worked on a project that went
one further than this - by
analying playlists (and/or
library/purchase data) from
many many many other listeners,
you could perform (arbitrarily
sophisticated) cluster analysis
on the playlists (using your
favorite diy mathematics
toolkit, or NetPerceptions, or
Andromedia or whatever). (Most
of the commercial tools don't
do a sufficiently good analysis
job, nor do they run to
completion in reasonable time
frames.) |
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Then, if you listen to three or
four songs, based on this
broader analysis, the system
could recommend more songs that
you'd probably like to hear. |
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The recommended songs could be
filtered based on what's
actually in your collection.
Or the system could go out and
napster/gnutella get the songs
you might like. |
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This works poorly when you only
do an insular analsis on a
single user's playlist -
there's not enough data to do
real inference. (we built this
and it sucked) |
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This also works poorly when you
only do superficial analysis of
the songs based on "category"
or "type". This leads to all
sorts of bad inferences, since
all
[jazz|pop|punk|rap|rock|whatever]
is not alike. (we tried it and
it sucked) |
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You can get real cross-category
recommendations that actually
have a high probability of
being correct. |
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We also implemented a scoring
system for feedback so that a
user could score the quality of
a recommendation, both on an
absolute scale, as well as
relative to the previous song
(e.g. Guns and Roses might be
good, but not juxtaposed with
the BeeGees). |
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Finally, it's not hard to see
how the crassly commercial (my
client, in this case) could use
such a system to make
recommendations for music to
purchase. (does anyone actually
purchase music these days??) |
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This is baked by the MP3 player Cymbaline (see link). |
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