The Netflix Prize and Production Machine Learning Systems: An Insider Look

I think the author is too constrained to promote Matlab, so the better (production-ready) alternatives are left out.

Furthermore, I do not understand why this is called an "insider look", when it is about someone reading a blog from 2012 and basically giving a recap.

Finally, I think it is time to move past this trope of "Netflix wasted 1 million dollars on a solution they did not even use". Probably the entire top 20 of the Netflix competition had a good enough score to build upon -- no need to focus only on the solution from the (rather lucky) winners. We are six years after this competition and the community is still talking about it. Now how is that for marketing?

There is no mention of Factorization Machines ( http://www.libfm.org/ ). A technique spawned with this competition which revolutionized recommendation engines and is definitely in use at Netflix now. Instead they keep harping on 'Collaborative Filtering' when that is a very basic technique (obviously supported by Matlab).

I miss a mention of power tools (Vowpal Wabbit has fast production-ready scalable matrix factorization which can fit on a 1 million dataset on a laptop: https://github.com/JohnLangford/vowpal_wabbit/tree/master/de... ) and of customers actually running Matlab based recommendation engines on scale.

If this is interesting then I recommend (ha!) the EdX Spark course[1]. One assignment shows how to build a recommender on the MovieLens dataset mentioned in this article.

[1] https://www.edx.org/course/introduction-big-data-apache-spar...

Is Google taking advantage of these techniques as well? (What one person clicked might be interesting for others with a similar clicking behavior as well).