This article came by way of SIG-IA mail list. Good description of Amazon's recommendations engine. Explains the difference between traditional collaborative filtering, cluster models, and Amazon's approach.

The key to item-to-item collaborative filtering's scalability and performance is that it creates the expensive similar-items table offline. The algorithm's online component—looking up similar items for the user's purchases and ratings—scales independently of the catalog size or the total number of customers; it is dependent only on how many titles the user has purchased or rated. Thus, the algorithm is fast even for extremely large data sets. Because the algorithm recommends highly correlated similar items, recommendation quality is excellent.10 Unlike traditional collaborative filtering, the algorithm also performs well with limited user data, producing high-quality recommendations based on as few as two or three items.

Link:
http://dsonline.computer.org/0301/d/w1lind.htm