Thanks a bunch! It took me a while to craft the solution to make sure it was both effective + private. I was originally inspired by Canopy. They built a news aggregator with private & personalized posts a few years back and the idea sat in my head.

To answer your question(s), there are quite a few signals that big tech uses to recommend content. Not all of them are privacy invasive (or at least they don't HAVE to be). My approach was to do thorough research on the different signals used by big tech to make their recommendation engines, and just build ones that 1.) were possible given fediverse API limitations and 2.) private. I had to craft some novel approaches to make it work but I'm pretty happy with the outcome!

One of the biggest differences between the "big tech" approach and Quiblr's is that most big tech does not keep data simply on your device. They store it in datacenters to build large social-webs to essentially cluster users (and push more relevant ads).

But I was able utilize many of the other signals used by big tech (e.g. communities you engage with, metadata of content you read, dwell time, post/comment/vote activity) and I designed it to work offline with no servers.

Edit: grammar