I have the impression that machine learning kind of torn between accomplishing things that humans can do but machines can't and trying to get useful information from inherently uninformative data sets.

I can see that recommendation engines are things that a lot of companies want but when recommendations just come from prior history, perhaps there are hard limits to how much any pattern finder can deduce. No doubt, one can define the problem and get better but the reason that adding more data in this case doesn't get you that much improvement is that you hit diminishing returns on your data.