Its not my view, that's what it is. No machine learning is necessary to generate that section. Just a lot of intersected purchases from a huge amount of customers, sorted by frequency.
Do you work at Amazon, so you know this is how it works? Or are you projecting how you imagine it works?
Because if I'm an Amazon dev I'm 100% not just showing a 45 year old woman who buys a TV the same "other people bought" as a 25 year old man. I'd definitely run the same ML on each section, because the whole point is to understand the closeness of connections between personal traits and purchases, and it is impossible for a human to outperform in that task.
I used to work at Amazon. Demographics aren't needed when you have a ton of customers for which to take the intersection, and sort by weighted (frequency, time of purchase.) Any non related purchases won't show up nearly as often or close in purchase date as related ones on a large sample of customers. This is just a consequence of having big data, which makes the noise floor insignificant. ML shouldn't be thrown at everything indiscriminately.
