If I recall, BellKor made many, many models based on Gradient Boosted Decision Trees, Restricted Boltzmann Machines, and kNN. They tried many different feature subsets, added temporal weighting, and tried many reduced-dimensionality representations (SVD, NMF). They then stacked them all together into one final ensemble whose RSME beat everyone else's on a hidden validation set.

In a production environment, this is probably an insane amount of transformation, feature extraction, and classification for marginally little gains in precision (as defined here). But I'm only a year or two in to building production-environment classifiers, and nothing at Netflix's scale (though not tiny either-- it is a problem if I can't do feature extraction and high-precision/-recall* classification within a few milliseconds).

* - mid 90s, for a hard NLP/social graph problem.