This is, by the way, the dirty secret of the machine learning movement: almost everything produced by ML could have been produced, more cheaply, using a very dumb heuristic you coded up by hand, because mostly the ML is trained by feeding it examples of what humans did while following a very dumb heuristic. There's no magic here. If you use ML to teach a computer how to sort through resumes, it will recommend you interview people with male, white-sounding names, because it turns out that's what your HR department already does. If you ask it what video a person like you wants to see next, it will recommend some political propaganda crap, because 50% of the time 90% of the people do watch that next, because they can't help themselves, and that's a pretty good success rate.
Pretty much. AI is just a mirror, and the twerps just don't like what they see.
The brilliant bit here is that each of the trackers has a bit of data about you, but not all of it, because not every tracker is on every web site. But on the other hand, cross-referencing individuals between trackers is kinda hard, because none of them wants to give away their secret sauce. So each ad seller tries their best to cross-reference the data from all the tracker data they buy, but it mostly doesn't work. Let's say there are 25 trackers each tracking a million users, probably with a ton of overlap. In a sane world we'd guess that there are, at most, a few million distinct users. But in an insane world where you can't prove if there's an overlap, it could be as many as 25 million distinct users! The more tracker data your ad network buys, the more information you have! Probably! And that means better targeting! Maybe! And so you should buy ads from our network instead of the other network with less data! I guess!
None of this works. They are still trying to sell me car insurance for my subway ride.
Recommendation engines are highly context dependent. I don't read news with the idea I'm gonna buy shit. I do watch videos with the idea I may watch more.
When I heard this was also when I learned the word "satisficing," which essentially means searching through sludge not for the best option, but for a good enough option. Nowadays Netflix isn't about finding the best movie, it's about satisficing. If it has the choice between an award-winning movie that you 80% might like or 20% might hate, and a mainstream movie that's 0% special but you 99% won't hate, it will recommend the second one every time. Outliers are bad for business.
Yet again reiterating that AI can't do better than recommend what we already empirically are shown to like. It's such a mirror we don't realize it later in the same article.