Tagging Robots: A Genuinely Useful Application of Social Media Concepts For DAM?

As reported on Tech Crunch, former Myspace employees, Todd Leloy and Joe Muoz have developed an automated semantic Tagging Robot which crawls Facebook newsfeeds and offers you relevant topics for each link.  Tagging Robot uses NLP (Neuro Linguistic Programming) and machine based learning to develop a profile of users and extrapolates data from Facebook interests and Social Graph.  It then presents a list of links which it thinks you might find interesting which you can track and follow.  The beta demo is a proof of concept demo for the API as Leloy and Muoz plan to integrate with other website:

“…eventually users will be able to integrate this functionality onto any site that has an audience and a content index, like a blog or social network. “It’s really hard to demo and API, so we build out the demo as proof of concept” , he [Leeloy] said.  “In a world where you encounter 500 links per day, you need to know what’s best from your social network and beyond.” [Read More]

I can envision many scenarios where this technology could be useful for Digital Asset Management.  There is the potential for asset downloading data, asset shortlists (carts) and metadata to be analysed using the API or RSS feeds that most DAM systems generate.  Technologies like Tagging Robot point the way for Social Media concepts to be integrated into DAM in a way that has far more practical application than just voting about assets or discussion facilities.

The Tagging Robot Posterous Blog contains more detail on the progress of this interesting project.

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