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Clicks are noisy and have their problems (especially clickbait), but as far as ranking goes they still blow out of water everything else you can use. Analemma_ is right on the money. We need more Lemon Pledge^W^W clicks!

Speaking as someone who worked for another big web search engine in the past (not G, not B)

And counting good/bad clicks for a given (query, url) pair is just the tip of the iceberg. There's a lot of other interesting stuff you can do with them _if_ you have the data. Deep learning/NLP with clicks as training signal is probably the most exciting area to me, to name one. Unfortunately almost all of the data currently gets nabbed by google, other search engines are just getting by with the scraps. And it's very hard to bootstrap a competitor from scratch - for example Cliqz had to resort to some shady deals with Mozilla just to get any data to start with.

The above heuristic arguments seem inherently weaker than the direct experience of someone who used to be on the team that improves the results. They are well-funded and should be able to back out the effects you mention.

How are they ever going to know whether they improved results for me or not? You might as well train AI to play a game without ever checking their score. Oh, it's spending 20% more time in each room now and firing fewer bullets than before. Surely it is a better AI now.

Sorry, appeal to authority is no argument. Direct experience is valuable if there's an argument or some real scenario we can dissect, otherwise it's nothing more than a baseless claim. Without concrete examples, it's not even an anecdote. And there are plenty of anecdotes about search results for in-depth content becoming harder and harder to find.

The person I was responding to posted that they're using this training data to order the top 10 results or so. That's already an indication that it's not very helpful for me. I don't get frustrated if the top few results are in suboptimal order, I get frustrated when I get pages and pages of garbage and irrelevant results and can't seem to get anything useful out of it.

A lot of this does, in fact, get solved through machine learning. If one search query is conducted a hundred times by different people with otherwise similar profiles, the machine learning can constantly modify the results until the hundredth person sees the best results first. For example, if the first 25 people all click on the 2nd, 4th, 5th, and 8th result and then stop, it can modify the order of the results for the next 25 people, and randomize the order, so that some see 8-4-2-5 first, and others see 2-5-8-4, etc. The it re-assesses the results and gets smarter.

The gaming part is where there's a lot of importance, since it forces the machine to learn which users are acting nefariously.

Shuffling the top 10 results is not fixing the big problem in search for me. I don't care too much whether the company I search for is in first or fourth place. But I'm really irritated when I get pages upon pages of useless results and nothing remotely relevant.

See the replies elsewhere. The metrics go beyond simple, naive "click counting."

Specifically they also measure metrics like "regret" - if you went back to the search results and chose something else this indicates it wasn't a useful search result for the query and this counts against it.

In theory this is quite a nice defense against clickbaity, content free results.

> 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.