http://www.geocities.com/CapeCanaveral/1624/bam.html

How about this? This code learns the relationships between "item a" and "item b" ... and then later on, when presented with a item c that is similar to item a, it can associate it to item b. Can it be the case such that as the user indicates preferences on transitions, the neural net will learn... but when the playlist mechanism picks the next song, it will query the neural net, and the neural net will return some percentage "ok'ness" and if it is less than 50% ok, the playlist will skip and try another song until a match occurs.

OR as some user suggested, have the neural net learn "not-ok-ness" and the playlist mechanism will learn inappropriate transitions, and perhaps after learning that it's not good to put a new age song between two heavy metal songs, it will acknowledge that relationship and suggest not putting any new agey stuff between metal stuff.

(neural nets don't take up a lot of space ;)

Calvin

Calvin