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I wish this was about the "Recommended" page, and why it doesn't work. Has anyone looked at this part of the open-sourced Reddit?

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.


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