Netflix is a bad example. After Netflix Prize they stopped trying to make a better recommendation system. They only used a few of the models from the winning ensemble, and now are using something even weaker instead.

That said, the competition is hardly any better. Most use the same collaborative filtering from the Netflix Prize (Koren et al), which by design never recommends anything new. Most new "big data, machine learning" personalized recommendations just give you most popular plus some random noise. They perform well on some very artificial evaluation functions, but rarely A/B tested on actual users. When they are tested, they barely beat naive models like IMDB top 250.