Just based on the experiences I have with them, I feel like the Big Problem with recommendation engines is that they're bad at teasing out why somebody liked something and end up falling back on an obfuscated equivalent of P(likes A | likes B), which ends up dominated by people obsessed with a particular genre, franchise, or theme. Or worse, it relies on extremely generic tags. If you buy, say, Steins;Gate and Rosenkreuzstilette on Steam, it's going to keep recommending the most garbage VNs and PG-13 nukige on the planet until the sun explodes because you obviously like VNs and anime (as opposed to quirky sci-fi stories and Mega Man).

There's a paragraph about this in the article that discusses automated identification of clusters, but it makes it sound like there was a lot of human judgment/guesswork in figuring out why any given set of movies clusters the way it does.

> Each new algorithm takes on average three or four hours to churn through the data on the family’s “quad core” Gateway computer.

Huh. I don't think I would have guessed that Gateway was still around in the days of quad-core desktops.