For years now I have been a huge fan of Pandora (sorry, not apparently available to all across the world due to digital rights issues). Even though Pandora’s Music Genome was not set up as a W3C-compliant ontology, in its use and application it is effectively one. What Pandora shows is that feature selection and characterization trumps language and data structure format.
Given that, I have also been a Web scientist as to how I select and promote music to meet my musical interests.
Thus, based on my own totally unscientific study, here are a few things I’ve found worth passing on. It would be bold to call them secrets; they are really more just observations:
- When a new “seed” artist is chosen, the attributes of that artist (up to 450 different attributes from beat to genre to dominant instruments) set the pure characteristics of that channel
- As similar songs play that meet this profile, when you vote them “up” or “down” you are effectively adding or deleting options for these 450 attributes in your profile criteria
- Thus, continuous expressions of preference as a channel plays acts to “dilute” the purity of the initial seed; these preference expressions lead to a “mixed” seed
- The more that choices are preferred, the more the signal of your original selection gets diluted.
The net result is that I now no longer vote any of my songs on any channel as up or down. Rather, I look to the purest “seeds” that capture my mode or genre preference. If my initial selection does not provide this purity, I delete it, and try to find a better true seed.
This approach has led to some awesome channels for me, that I can then combine together, depending on mood, into mixed shuffle play with randomized channel selection. I no longer vote any song up or down; I rather look for more telling seeds.
As a couple of examples, here is a channel of less-well known 60’s goldies and a jazz guitar channel that are pure, single-seed channels, that, at least for me, provide hours of consistent music in that genre. Roll your own!
