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Crunching the Math On iTunes
markmcb writes "OmniNerd has posted an interesting article about the statistical math behind iTunes. The author makes some interesting observations concerning the same song playing twice in a row during party shuffle play, the impact that star ratings have on playback, and comparisons with plain old random play (star ratings not considered)." From the article: "To test the option's preference for 5-stars, I created a short playlist of six songs: one from each different star rating and a song left un-rated. The songs were from the same genre and artist and were changed to be only one second in duration. After resetting the play count to zero, I hit play and left my desk for the weekend. To satisfy a little more curiosity, I ran the same songs once more on a different weekend without selecting the option to play higher rated songs more often. Monday morning the play counts were as shown in Table 1."
I wish iTunes would get ratings from some online source much like it gets tracknames from Gracenote. Can you imagine a server of user-submitted ratings? You could opt to use an average rating from all users, or a rating from users with particular tasks (i.e., if you are a metaller, then you'll probably not want raver's musical opinions affecting your ratings!).
Why? Because I haven't got the time to go around rating my entire music library. Judging from that article, it is dangerous to only do a few because of the weighting algorithm used - surely it would be more sensible to assume that 'not rated' meant 3 stars rather than 0 stars? That way you could rate down shitty songs, and rate up excellent songs, but ignore rating the vast majority of songs.
Maybe songs need more than one rating.
Rating For Morning Listening (* for Aphex Twin, Slayer, etc)
Rating For Afternoon Listening (**)
Rating For Evening Listening (****)
Rating For Party Listening (**)
Rating For ${mood} Listening
Then instead of getting work done we can spend out entire lives rating music.
That's exactly why I love last.fm (formerly Audioscrobbler & Last.fm). It automatically tracks what you listen to and then allows that information to be used to give you neighbors in the music world based on what interests you have in common. You can add friends, join groups, and even tag your music. All of this is extremely useful in finding new stuff. They've got plugins for all the major media players (and even some minor ones).
;)
Add on top of that the ability to play a custom-built radio station, set it to play only new music or listen only to music from a particular user profile.
Linux and BSD supported! Open source plugins and radio station player! Could it get better?
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but make sure that the last line
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Your decimals look more like the pricing model than the weights for playing songs..
.285 -- $299, iPod (full?) 20gb .238 -- $249, iPod mini 6gb .190 -- $199, iPod mini 4gb .143 -- $149, iPod shuffle 1gb .095 -- $99, iPod shuffle 512mb
5 star -
4 star -
3 star -
2 star -
1 star -
"Victory means exit strategy, and it's important for the President to explain to us what the exit strategy is." G.W.Bush
A friend of mine who worked at a radio station that played a very diverse range of music told me how they select music.
She said that research had shown that listeners would rate the same song higher if it followed other song of a similar genre. If they play songs of different genres randomly the listener does not enjoy the music as much.
So their tendency is to play "blocks" of music.
For example....
4 Classic Rock songs
3 Blues Songs
3 Folk songs
4 Female Rockers
3 Grunge
etc.
This is common knowledge in the radio world. I wonder if Apple has incorporated this type of logic into it's iTunes algorithms?
The radio station in question is WXPN and can be found under iTunes > Radio > Public > WXPN
I use the stars to indicate how often things should be played.
* - Never play. It's only in the list for the sake of completeness (I hate having partial albums)
** - Play very rarely. If I'm in the mood, I might listen to it.
*** - I'll listen to it at least once a week. If it comes up randomly on the shuffle, I won't take it out of the list.
**** - I can listen to this several times in a day.
***** - I'll listen to this song anytime, anywhere. If it comes up twice in a row, no problem. If my playlist only has this song on it, I can cope with that for at least a few hours.
This means that I have to periodically re-rate the songs. That seems only reasonable, though. Why would songs stay at the same rating forever? As the novelty wears off, I can relegate a song to 4 or 3 stars.
I also keep extensive smart playlists that make sure that songs that are 3 stars or less only get played once every few days.
points(0 stars)=1
points(1 stars)=3
points(2 stars)=4
points(3 stars)=5
points(4 stars)=6
points(5 stars)=7
probability(X stars) = points(X stars) / 26
This yields the following probabilities, listed along side the observed values from the article along with 95% condience intervals.
p(5 star)=.2692 [.270 +- .0038] .0036] .0033] .0031] .0027] .0016]
p(4 star)=.2308 [.230 +-
p(3 star)=.1923 [.189 +-
p(2 star)=.1538 [.154 +-
p(1 star)=.1154 [.118 +-
p(0 star)=.0385 [.039 +-
As you can see each computed probability falls within the 95% confidence interval, so there's a good chance this is the correct forumla.
Boy do I have too much time on my hands today.
But all this talk of 0, 1, 2, 3, 4, 5 has me thinking of another rating system. Would anybody care to do an analysis of the ratings in Slashdot comments? What are the relative populations (I expect a ton of 2's but how about the rest)? Do comments made in the first hour after a story is posted stand a better chance of reaching +5 than comments made later in the day?
One of my gripes about the Slashdot comment system is that it discourages contemplation and discussion. Comments made more than 24 hours after a story is posted are rarely read and almost never moderated. This is in contrast with comments system like Usenet or other bulletin boards, where threads can remain lively for weeks.
AlpineR