Elias Bizannes has posted a great follow up to Marjolein's Attention Profiling overview. While Marjolein explained Attention Profiling in general and walked through the user experience of using Engagd, Cluztr and/or Dandelife to get one.

Elias has dug deeper into the spec itself to explain the type of information you APML can actually store about you and how it maps to the real world.

From his post:

APML - the specification
So all APML is, is a way of converting your attention into a structured format. The way APML does this, is that it stores your implicit and explicit data - and scores it. Lost? Keep reading.

Continuing with my example about Sneaky Sound System. If MySpace supported APML, they would identify that I like pop music. But just because someone gives attention to something, that doesn't mean they really like it; the thing about implicit data is that companies are guessing because you haven't actually said it. So MySpace might say I like pop music but with a score of 0.2 or 20% positive - meaning they're not too confident. Now lets say directly after that, I go onto the Britney Spears music space. Okay, there's no doubting now: I definitely do like pop music. So my score against "pop" is now 0.5 (50%). And if I visited the Christina Aguilera page: forget about it - my APML rank just blew to 1.0! (Note that the scoring system is a percentage, with a range from -1.0 to +1.0 or -100% to +100%)