this post was submitted on 18 Jul 2023
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The thing is, a 10% upvoted post on a 10,000 people community is more popular than a 90% upvoted post on a 1000 people community - those 10,000 people in the former community are 10,000 people interested enough in that kind of thing to subscribe, whilst only 1000 people are interested enough in the other kind of thing.
So it does make sense to put the former higher up in the global page when sorted by popularity because globally that post was more popular.
However I do think there should be someway to as a user push down posts from certain communities without outright blocking the whole thing: maybe som throttling-down based on the rate of posts per time (i.e. the upvote threshold for posts from a community to come out in All depends on the number of otherwise qualifying posts in the last X days/hours, thus explicitly targetting the "flooding with posts" itself) rather than the straight count of upvotes or the proportion of upvotes that you suggest.
That said in the meanwhile I'm really tempted to block the more generic meme communities.
General popularity is not a good metric IMO. If I like a community, then it shouldn't matter if a million people like or it's only me and my cousin. If the community likes the content, I want to see it.
It's trust between the members of a community.
However, weighted sorting is not a solution too, upvotes counts are not linear. Maybe, quantile sort?
What's liked by the general population is a good metric for providing general stuff to the general population and that's what we're talking about in All.
That average can however deviate a lot from the sweet spot for some people, quite possibly a large minority (even the majority depending on how concentrated or not people's tastes are around it).
Something that looks at your previous choices (or even generally stated choices in the form of communities you subscribe to or block) similarly to what some search engines and some social media sites will do, can shift that toward more your own specific tastes, but that's computationally more expensive and requires more users and more user data to get better results (basically it's finding certain kinds of users and local minima which are more satisfactory to them).
I suspect something like an AI solution (not LLM, just a much simpler neural network) running on your own device that tries to predict what you're going to click on and learns with what you do (or not) is the only way for a personalized "no fluff on my feed" solution, but that's for apps running on top of Lemmy, not the Lemmy engine.