- cross-posted to:
- fediverse@lemmy.ml
- cross-posted to:
- fediverse@lemmy.ml
I found this link aggregator that someone made for a personal project and they had an exciting idea for a sorting algorithm whose basic principle is the following:
- Upvotes show you more links from other people who have upvoted that content
- Downvotes show you fewer links from other people who have upvoted that content
I thought the idea was interesting and wondered if something similar could be implemented in the fediverse.
They currently don’t have plans of open-sourcing their work which is fine but I think it shouldn’t be too hard to try and replicate something similar here right?
They have the option to try this out in guest mode where you don’t have to sign in, but it seems to be giving me relevant content after upvoting only 3 times.
There is more information on their website if you guys are interested.
Edit: Changed title to something more informative.
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deleted by creator
you should consider moving to Facebook or Threads, maybe?
Not an option
As for the rest yeah those do seem like genuine obstacles. Partially think the reason I liked the algorithm is because it reminded me of the Web of Trust things like Scuttlebutt use to get relevant information to users but with a lower barrier to entry.
Also as I’ve said elsewhere it doesn’t have to be this exact thing but since this is a new platform we have the chance to make algorithms that work for us and are transparent so I wanted to share examples that I thought were worthwhile.
Edit:
You’d also turn Lemmy into the strongest echo chamber you could possibly create.
PS. I don’t think that’s true. Big tech companies that have more advanced algorithms would probably be much better at creating echo chambers.
deleted by creator
No, not as simply as that. That’s the basic idea of recommendation systems that were common in the 1990s. The algorithm requires a tremendous amount of dimensionality reduction to work at scale. In that simple description it would need a trillion weights to compare the preferences of a million users to a million other users. If you reduce it to some standard 100-1000ish dimensions of preference it becomes feasible, but at the low end only contains about as much information as your own choices about subscribed to or blocked communities (obviously it has a much lower barrier of entry).
There’s another important aspect of learning that the simple description leaves out, which is exploration. It will quickly start showing you things you reliably like, but won’t experiment with things it doesn’t know you’d like or not to find out.
There’s another important aspect of learning that the simple description leaves out, which is exploration. It will quickly start showing you things you reliably like, but won’t experiment with things it doesn’t know you’d like or not to find out.
Why would this be the case? It shows you stuff that people who like similar stuff that you do like, but people have diverse interests so wouldn’t it be likely that the people that like one thing like other things that you hadn’t known about and that leads to a form of guided exploration?
There’s two problems. The first is that those other things you might like will be rated lower than things you appear to certainly like. That’s the “easy” problem and has solutions where a learning agent is forced to prefer exploring new options over sticking to preferences to some degree, but becomes difficult when you no longer know what is explored or unexplored due to some abstraction like dimension reduction or some practical limitation like a human can’t explore all of Lemmy like a robot in a maze.
The second is that you might have preferences that other people who like the same things you’ve already indicated a taste for tend to dislike. For example there may be other people who like both Boba and Cofee but people who like one or the other tend to dislike the other. If you happen to encounter Boba first then Cofee will be predicted to be disliked based on the overall preferences of people who agree with your Boba preference.
It might be easier to have posts be given tags and weights and have up voting and down voting change a users tags and weights and maybe have new content sorted by closeness to the users vectors space.
That way you aren’t having to track every event but instead having events update the objects values.
That would be my thought at least. Though I would think it would be best that users could sliding scale the effect. As in let the user determine how “aligned” they want the posts they see to be with them.
easier to have posts be given tags
If this is not being done automatically by the server by analysing the content, people will not use tags, or use irrelevant tags, or fill it with tens of tags like Instagram’s early days or whatever else I cannot think of now. But I think it is not easy to work as intended
