[hombre_fatal]

Well, people always have examples of fail cases. My friend at Amazon was dealing with the problem of black socks being tagged as DSLR cameras. It would have been too soon to close the curtains on ML just because that's obviously wrong.

Same with bogus results in Google search. It would be a mistake to fixate on a fail case at the expense of seeing what it gets right.

One thing that can be said about Amazon is how data-driven it is. Even an obvious "improvement" to a system would require analysis to back it up as an improvement. For example, it might seem obvious to filter out lower quality user-created answers in the product FAQ, but answers with poor grammar might actually boost sales because shoppers trust the answer more.

Also, as we descend deeper into ML/AI and black boxes, the deeper we get into effects from afar. There's no real place to write if (user.sex == M) then weigh('tampons', -1) as it was a constellation of factors that cascaded into a man seeing tampons like that time he purchased something related for his girlfriend. The next rung in line is the business of mind-reading.

[Boxxed]

I still hate how I'll buy something that is clearly an expensive one time purchase from amazon (such as a lawnmower or an audio receiver) and the best its dumb algorithm can come up with is, "Hey, wanna buy another lawnmower?"

[sidkshatriya]

Actually this recommendation makes a lot of sense. You generally have 30 days to return your purchase, no questions asked. The algorithm knows that you're now quite likely to buy another lawnmower/audio receiver. Perhaps there is a fat chance you may even spend more the second time over (after returning your previous purchase)?

[setr]

Since Amazon knows whether you’re submitting a return.. they could just filter to that scenario and then provide such recommendations.

[londons_explore]

There's a good chance you purchase the 2nd one before returning the first.

Or you sell the 1st elsewhere like Craigslist.

Either way, the numbers show those ads (remarketing ads in industry speak) are insanely effective.

[X6S1x6Okd1st]

They might be weighting by E(rev|ad_shown) in which case you'd only need a very small amount of repeat users to make it worthwhile to show it to everyone.

Not that that justifies the practice.