I've built a recommender system using Python Surprise library.
Next step is to update algorithm with new data. For example a new user or a new item was added.
I've digged into documentation and got nothing for this case. The only possible way is to train new model from time to time from scratch.
It looks like I missed something but I can't figure out what exactly.
Can anybody point me out how I can refit existing algorithm with new data?
Unfortunately Surprise doesn't support partial fit yet.
In this thread there are some workarounds and forks with implemented partial fit.