Computerised critics could find the music you'll like
MOST of us know better than to accept advice from an ignoramus. Yet we are content to listen to music playlists or even to buy tracks based on a computer's assessment of what we might enjoy, despite the software not knowing the first thing about music. The plan now is to make this less of a hit-and-miss affair by giving computers a course in musical appreciation.
Online music stores base their recommendations on similarities in buying or browsing patterns: when you buy a song, for example, the system might highlight music purchased by others who have bought the same song as you. Other online music services, such as Pandora, combine this so-called crowd-filtering approach with human labelling - where users assign descriptive terms to a song. The system can then choose songs for you whose descriptions match those of a track you have already selected, for example.
http://www.newscientist.com/article/mg20627644.900-computerised-critics-could-find-the-music-youll-like.html
Online music stores base their recommendations on similarities in buying or browsing patterns: when you buy a song, for example, the system might highlight music purchased by others who have bought the same song as you. Other online music services, such as Pandora, combine this so-called crowd-filtering approach with human labelling - where users assign descriptive terms to a song. The system can then choose songs for you whose descriptions match those of a track you have already selected, for example.
http://www.newscientist.com/article/mg20627644.900-computerised-critics-could-find-the-music-youll-like.html
