There should be an open-source recommendation algorithm, though; I’m sure of it.
Problem is that the kind of algorithm you envision is technologically a black-box, not just by choice. It’s a machine learning model. At best, you could make the training data and instructions public, but it would still be hard to reason why it makes certain decisions. Corporations traditionally try to eliminate biases by throwing as much data at it as possible, but that makes it even harder to reason about it.
I guess, maybe you could try to split the tasks. So, set up a list of e.g. 50 topics, such as sports, IT, politics etc… Then use a small language model to decide into which categories each post fits. And then you could let the user decide the weights for the topics + weights for recency and vote count.
Or I guess, automatically decide the weights based on what the user upvotes and then make the weights transparent to each user.
But yeah, I don’t think there’s prior art in this respect, so would probably need lots of experimenting still.




Right, yeah, I guess on Lemmy, the categorization is already mostly there. I was thinking more generally… 😅