LinkLonk
linklonk.com

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:

  1. Upvotes show you more links from other people who have upvoted that content
  2. 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.

    • statue_smudge@lemmy.worldEnglish
      7·
      11 months ago

      Storing it as a sparse graph should reduce the storage requirements drastically, since most edges wouldn’t exist.

    • Danterious@lemmy.dbzer0.comOPEnglish
      5·
      11 months ago

      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.

    • criitz@reddthat.comEnglish
      1·
      11 months ago

      Instead of comparing every single individual users votes with every other one, you create clusters using data science techniques and bucket all users into those clusters, which are calculated on a nightly or weekly basis. By controlling the cluster size you can keep the number of comparisons managable, and still achieve OP’s vision.