> Specifically they also measure metrics like "regret"

I explicitly addressed this:

> clicking on many results can indicate that they're bad (I click another result because the previous wasn't good), or because they're good (I'm browsing choices).

You don't know the reason why I clicked back. Maybe I got what I needed at a glance, and I'm browsing to the next thing to see if there's more of what I need.

You don't know that the Average Joe (and a million other Average Joes) wasted 15 minutes on clickbaity content free crap despite not getting anything useful out of it. You know, those same SEO spam sites are trying to find ways to make the user stay as long as possible; they are in the game. And I'm sure there are plenty of gullible users who will stay. Who knows, maybe SEO spam is working and that's precisely why we see SEO spam sites in results?

Again, I think training like this is pretending the problem is much simpler than it is. And I think that there is a real danger that training like this just optimizes for 1) average users 2) sites that managed to game the system with their SEO. Neither is optimizing for quality results.

That theory has so many holes and pitfalls.

I just think you're assuming these search engines are using way fewer factors than they actually use. It's probably exceptionally easy to tie your search behavior to someone else who's much more like you than "the average", unless you're using a search engine like DDG, Qwant, etc.

Maybe the best bet is for a search engine to try to include a simple, non-intrusive method of getting user input to determine the usefulness of a search result. Like, if it determines you came back as a "regret", maybe a ThumbsUp/ThumbsDown icon next to the last link you clicked to get you to say whether that link was useful in any way, or maybe a "block" option as well to say that you never want sites like that again.

> I just think you're assuming these search engines are using way fewer factors than they actually use. It's probably exceptionally easy to tie your search behavior to someone else who's much more like you than "the average", unless you're using a search engine like DDG, Qwant, etc.

I should, then, expect dramatically different results when I use someone else's PC or a public computer. I have not witnessed dramatic differences.

It's just you pretending search engines are simpler then they are. Noone's just ranking most clicked sites first - that's too naive and encourages clickbait. Clicks are an ingredient in the overall ranking system, which is generally also trained on relevancy labels by human annotators/raters/assessors. These human labels are far more reliable and can penalize all the bad clickbaity stuff which clicks can't, but unfortunately also very expensive to obtain and thus low volume.

We may not know exactly why you clicked back, or why Joe wasted 15 minutes on a site, but from all clicks in aggregate and from the human-labelled data, we can tell how that correlates with the clicked site's relevancy and quality and utilize your clicks accordingly to improve the ranking.

Finding such correlations is still by far and large what all search engines do, rather than trying to truly understand the query. It's only now starting to change with recent advances in deep learning and NLP.