Great link! Google has some great research publications as well that I recommened everyone checkout: http://labs.google.com/papers.html<p>They have an amazing collection of research papers based on their products. Personally, I recommend "Evaluating similarity measures: a large-scale study in the Orkut social network" as a preliminary mathematical introduction to user interfaces.
The problem with collaborative filtering is that there's a lot of magic going on that the user does not understand. Some users like to have more control over personalized recommendations and social networks can give them that control.
From what I can tell a lot of this CF stuff starts with a machine learning algorithm and data about likes/dislikes, making filtering a classification or search task. Is there something that differentiates it from the usual machine learning challenges? Like dealing with users interactively, perhaps?