I am Marc, a PhD student working on Privacy-Preserving Machine Learning (with a slight focus on Federated Learning). Being a Fediverse enthusiast, I am looking for some applications of my research work in the Fediverse. My will is to bring ML to the Fediverse. Don’t get me wrong, I don’t want to force ML features in the Fediverse but only cover necessary features (if exist). In this context, Peertube might be a good fit if you want to build a privacy-preserving recommender system: such system would be collaboratively trained by all instances (i.e., all instances accepting the recommendation extension). No private data would be revealed (= each instance keeps its data locally) and each instance might even have a slightly personalized model (depending on the particular interests of its community).
This might be already too much details since my first question for you is: « Would the Peertube project/developers be interested by such automated recommender system? » I saw a relatively old open issue about recommendations on GitHub but I would like to know whether informal discussions happened since then. Maybe, you even have some developers working on it?
NB: I first posted this message on Matrix and later discovered this forum which is clearly a better place to discuss such complex feature.