Whilst waiting for my plane this evening I realised that Advogato already possesses sufficient data to be used as a reputation space. The certifications and the diary ratings together comprise a 2-d reputation space which can be charted.

The basic algorithm looks something like this:

#assume we have a 2-d array of points
for each person in personList:
	r = person.rating
	c = person.cert
	
	points[r][c] = person

Once you have that then finding out who is in your reputation neighbourhood/cluster is trivial. Of course all this proves is that you can massage multiple independent reputation systems together to create a 'space'. But with other inputs it does show how this could be used for finding like-minded people or community-formation or collaborative filtering. The real problems start to show up when you're trying to visualise n-dimensional reputation spaces and provide a usable interface.

A basic implementation in Python can be found here: http://www.oshineye.com/software/advospace.html and an example of it's output using myself as the root for the diary ratings can be found here: http://www.oshineye.com/software/advoSpaceOutput.html

Searching for Lenny Foner and his (seemingly dead) Yenta project on CiteSeer has generated a large stack of pdfs that I shall be going through sooner or later. Most of the interesting ones are using agents or similar notions to avoid centralising all the information for the trust metric in one place. It also gets arounds a few of the scaling issues as well.