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Emerging Tech: Attention Profiling Mark-up Language

Attention Profiling Markup Language (APML, APML.org) aims to create a stardardized means of collecting, importing, and exporting attention data between web applications (found via: Dallas Freeman).

“Attention Data” is essentially your clickstream - i.e.: what Amazon collects to make recommendations to you based on what you’ve looked at in the store. APML aims to standardize the collection of such info into a portable “attention profile” which can be exported and moved among services much as OPML allows you to move your subscriptions from BlogLines to GReader.

The point implicit in APML is that a user’s attention profile has intrinsic value and utility - and that it would be worthy synchronizing across services (eBay, Pandora, Google Reader, and Amazon, for instance), or exporting when migrating from one service to another. This speaks to the growing importance of discovery services: as the breadth and depth of content availabe on the net in general and on individual mega-sites like Amazon becomes un-parseably huge, automated recommendation/discovery systems will become one of the primary means of finding content and enabling transactions.

Its a very conceptually appealing idea - if you’re a technical person familiar with the concepts of attention data and discovery. Before its relevant for the consuming public, some marketing people will have to wrap a layperson-friendly vocabulary around the concept.

Final note: APML is FOSS.

APML is positioning for user-control in a discovery-centric net future. Someone out there is thinking ahead - good job!

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