Yes. See my explanation in the reddit thread. Basically, after orthogonalizing a set of feature vectors for dimensionality reduction, the resulting landscape of posts is clustered (k-NN as far as I can tell) and the 'closest' set of 'hot' posts to a user is returned. I'll be better able to fuss with this after I have a little more free time (eg. after my exam and the paper I'm working on); the code is primarily in Recommender.cpp if you have checked out the r2 git repo.
Using an unsupervised clustering algorithm instead of a supervised algorithm was, in my opinion, the Wrong Way to Go. After I get done with my screening exam this week, I am planning to screw around with it and maybe see if libSVM will offer a means of constructing arbitrary discriminators based on the selections of, say, one's favorite users, or one's own feedback.
Obviously there are a great many nits that need to be worked out with my idea, but I figure it may be worth a try.