And this picture helps too: shows the decay in ranking scores for posts of different popularity (score) over time.

After a day or so, the curve flattens out. This probably explains why we keep seeing posts that are months old in "hot" - if not enough new material is being posted, after the first few pages of "hot", posts that are 5 days old and 5 months old are essentially the same due to the exponential decay function that was chosen.

That page gives this equation:

Rank = ScaleFactor * log(Max(1, 3 + Score)) / (Time + 2)^Gravity

Score = Upvotes - Downvotes
Time = time since submission (in hours)
Gravity = Decay gravity, 1.8 is default

My guess is that the "gravity" parameter is the issue at the moment. Something is needed to make the decay less steep, so that really old posts aren't making it up to the top of the feed.

There might be some way of tuning the gravity parameter dynamically based on how much content is being submitted, perhaps aiming for something like "the average age of the first 200 posts should be 10 days" (I made those numbers up, but the basic idea would be that the time decay should be steeper when lots of content is submitted and less steep when content is infrequent?)

Thank you.

Thanks 😊

And this is a great thing about open source software

Want to know how something works? Want to know the implications of something, or whether it is artificially manipulated? You can go directly to the code.

How does the algorithm work for other software, and is it authentic and not manipulated for other gains? Nobody knows except them, and bad stuff can be hidden away.

Can someone who knows PL/pgSQL help parse this line:

return floor(10000*log(greatest(1,score+3)) / power(((EXTRACT(EPOCH FROM (timezone('utc',now()) - published))/3600) + 2), 1.8))::integer;

It seems to me that the issue might be that the function returns an integer. If the scaling factor is inadequately large, then floor() would return zero for tons of posts (any post where the equation inside floor() evaluates to less than one). All of those posts would have equivalent ranks. This could explain why we start seeing randomly sorted old posts after a certain score threshold. Maybe better not to round here or dramatically increase the scaling factor?

I'm not sure what the units of the post age would be in here, though. Probably hours based on the division by 3600? And is log() the natural log or base 10 by default?

In any case, something still must be going wrong. If I'm doing the math correctly, a post with a score of +25 should take approximately 203 hours (assuming log base 10) before it reaches a raw rank score of < 1 and gets floored to zero, joining all of the really old posts. So we should be seeing all posts from the last 8.5 days that had +25 scores before we see any of these really old posts... But that isn't what's happening.

I don't think any of the algorithm is expose to other instances so that wouldn't impact the communication between instances. At the end of the day this is open source so admins can freely build a forked version of Lemmy with a slightly different algorithm.