Feedback

I don't get the sparsity problem. Every user has only seen a small proportion of the items, and each item has only been seen by a small proportion of the users. That *is* the problem; why is it a limitation of the collaborative filtering systems? What you might mean is the dual of the early rater problem; when a new user comes they have very few ratings.

It would be good to refer the solutions back to the problems. Which solutions are designed to solve which problems?

For the DoppelgangerBots, please separate out the definition of TFIDF 1-3 (I think) from what they are doing with TF-IDF. (I know that TF-IDF is, but I can't work out what this is doing).

In the definition of RipperBot, how does it decide if an instance "classified the movie as high"? Presumably it has a learning algorithm that gives 0/1 predictions; you should tell us what this is.

GenreBot makes no sense to me. (Or are they only used as features for the linear regression = Mega-GenreBots?).

I am finding it difficult to parse the results (I am not sure 4 means). "Rejected" is meant in a very technical aspect; it does not mean that we should believe the negation. It really is a statement about the sample size than the truth of the hypothesis.

Overall, the page needs more intuitive explanations. What is the intuition behind each method, what problem is it trying to solve and why would we expect it to solve that problem?