I think it was supposed to cluster users who often voted the same way, and then infer that if a user voted up an article, most people in the same cluster as that user would also like the article.
I wonder why it didn't work. They certainly have enough of a dataset to play with by now. I wonder if it's computationally too intensive to continually update each user's results, or too difficult to come up with a good algorithm.
Does this "recommended page" problem bear any resemblance to the Netflix prize problem?
Creating a good recommended page would certainly lure me back to reddit.
I don't think the founders have much of an incentive to change reddit a whole lot. Also, the existing algorithm is definitely computationally intensive: it takes 12 seconds to generate a new recommended page.
