I don't think the actual winning algorithm itself was used, because real world systems have more constraints/requirements than what the recommender was trained on. But that was in 2009, pre deep-learning/AI summer, and $1 mil clearly helped stimulate interest in that area.

Today we see multiple billion dollar recommender systems, like Tiktok. Netflix ironically benefits the least from recommenders due to the nature of its dataset (Very expensive, low sample size).

My understanding was that their research on what drove engagement shifted quite a bit. Things like social proof, and product patterns like auto-loading the next episode to binge drove engagement metrics. Recently there were some articles about their team custom-identifying which cuts of a video to show as a trailer maximized engagement on a personal level. In some sense that is a recommendation, but it is a broader problem space.

Social proof on Netflix?

Yeah, the top10 in your region carousels get high engagement

I have a more cynical take; the recommendations declined when Netflix started producing their own content. Prior to this, what constituted a "good recommendation" was aligned between Netflix and the customer, but afterwards not so much.

Today Netflix is in the "how do we get our customers to use our service as little as possible but still pay us every month" phase of their mediacom hypocracy. From a business standpoint, that is their best optimization. They are AOL/TW from 20 years ago.

Matrix factorization was popular in industrial recommender systems for years afterwards, and its ideas are still useful today. https://sifter.org/~simon/journal/20061211.html

Netflix got enormous amounts of R and D for dirt cheap