Now that Iris launched nostr-relaypool-ts, and it's working great for retrieval, I started to focus on what I originally was interested in: ranking. So far it's in the planning stage, but I think it's going well. For now I'm planning on developing a pLike | note model (predicting whether a note is going to be liked by a user). I'm planning to use logistic regression with the following signals as a start: - time passed since note was created - note's author is followed by user - number of likes - number of comments - share of likes from the author by the user in the past - does it contain image? - does it contain link? - does it contain video? - text length - likes by followers Some are easier to implement, some are a bit harder, and of course I'll check their impact before launching them. I think I will order threads by the maximum probability that a note has in a thread. Also pLike can be used as a filter for comments to be shown / hidden. Of course pComment model can be trained on the same signal.