I'm sort-of trying to solve this with a recommender system approach.[1] Search isn't my primary objective; I'm mainly focusing on the case where you want to be made aware of relevant information on an ongoing basis but you aren't necessarily looking for something specific.
But that involves building up a ratings database of lots of different URLs. If it becomes really popular, I'd like to experiment with search. It could start with a "normal" search algorithm but then adjust the results based on rating data. E.g. "the top result for this query is X, but people with similar rating data to you always down vote that link, so we'll move that result down".
I've heard people complain about how Google gives you personalized search results sometimes, but I'm not sure if the approach is actually bad. Perhaps it could work if it was focused on from the start instead of bolted on.
I've never got around to doing it and one of the reasons (excuses) is that I haven't yet figured out a way to stop bad actors exploiting it by, e.g., mass downvoting URLs of competitors. I did think about some sort of moving average, or bias toward more recent votes, which would at least somewhat mitigate against transient actions of this kind, but it's harder to deal with a sustained attack like this.
That is still exploitable: if I know you like A, B, and C, (perhaps those are popular items), I could also upvote those items and then downvote another item D. I have an idea for countering this: keep track of history. So instead of just measuring ratings overlap ("of items you've both rated, 75% of items were rated the same"), you keep track of the performance of recommendations made from that user ("of Alice's items that we recommended to Bob, Bob upvoted them 25% of the time").
I haven't thought about this super deeply yet, but I think to beat that, you'd have to actually provide valuable data to the system in the first place.
I would think the first case would result in way better data (which is why (I think) last.fm recommendation system is so good), but the latter case seems to be the natural thing to do, humans love to judge shit without actually engaging with stuff.
That brings up a key point which I'm not sure is clear to users or not: voting on items specifically means "this is/is not a good recommendation for me". It's not a judgment on the goodness of the item in general. So if findka showed me e.g. an article on pottery that looked high quality, I'd probably still downvote it since I'm not into pottery.
[I hated this] [I'm interested in this] [I liked this]
Maybe this does happen but it not weighted highly enough against SEO.
For instance, quite a few times I'll open a page in Google's results, scan the content and go back to compare it to another result. That doesn't necessarily mean I hate the first page or think it's terrible, just that I wanted to see what it had to offer.
It can also mean I did find the answer there, and am returning to Google to search for something else instead.
It's tough to distinguish cases like those from 'the content was bad' and 'the content was not what I was looking for'.
There's also the worry that it might end up hurting user incentives too, or alter the way in which users browse the internet as a whole.
After all, since finding out that YouTube rewards/punishes creators based on watch time/how much of the video someone watches, I'm now hesitant to even watch many videos at all, since I know some guy's career might potentially be put on the line.
Perhaps Google worries other people will be the same, and stop clicking as many results knowing that Google might punish the affected websites for not staying long enough.
They could hook this into Google Analytics and subtract the time you spent with that tab/window not focussed from your session duration.
I do fundamentally agree that session duration may not be the worst proxy for worthwhile content or, at least, engaging content, snd I think Google may use it as such.
This and the fact that Google search is actually as dumb as a sackful of hammers.
Don't get me wrong, for the longest time Google's results were "good enough", but Google has no real concept of the quality of a result (they've tried and it worked for a while - e.g., PageRank). Still, fundamentally their algorithms aren't smart enough to figure out whether any given piece of content is intrinsically worthwhile.
That's really important in a world where most content is, regardless of Google's guidelines, primarily written for the benefit of GoogleBot, to harvest clicks or drive traffic.
Unfortunately Google has literally zero incentive to change this situation (because they're making an insane amount of money off the back of the status quo), so they won't[1].
Logically then, the time is ripe for a new search engine to disrupt but the problem - that of determining intrinsic worth - is really hard to solve, so as yet nobody has, and Google still reigns supreme as the most popular search engine.
[1] It is also possible that I'm being too harsh here: they might be trying to change it - perhaps because they're worried about eventually being disrupted - but, as noted in the following paragraph, the problem is REALLY hard.