As Eliezer said - they implemented the two most important algorithms from the contest, which advanced the state of the art in recommendation systems & gave the

As Eliezer said - they implemented the two most important algorithms from the contest, which advanced the state of the art in recommendation systems & gave the majority of the benefits, and didn't implement the long tail of algorithms that each only gave a very slight marginal benefit & would have been costly to re-train and maintain. This is one of the advantages of running shorter competitions: normally it takes 1-3 months to approximately hit the asymptotic level of performance on a dataset given the inherent noise in it and the state of the art in machine learning. The shorter competitions are focused on finding the low-hanging fruit that generate large improvements (such as SVD & RBM's in Netflix's case) and exploring the space of possible model structures, as opposed to optimally ensembling across a large number of models to eek out the last 0.01% of performance. Exploring the space of useful features & possible models enables you to trade off computational efficiency & maintainability vs. model performance in production as well. The $1 million dollars Netflix put to the prize leveraged >> $1 million in human effort to explore the possible models, from which they found and applied the two best suited for their production implementation. (disclaimer - I work with Kaggle)