I have data where companies ask users to score a bunch of questions but some items may be randomly chosen while others are personalized. Users score higher in personalized questions on average. I have a user ID, question ID, corresponding score of the question by the user, and whether the question is random or personalized.
I want to build a recommendation system that incorporates the feature of a question being random or personalized.
I assume that for a personalized item to appear there must be some learning of previous random questions the company learned about the user beforehand.
But I got quite lost in terms of how to have a recommendation system that incorporates the dynamic structure.
I know basic recommendation includes matrix factorization or embedding for a user-item matrix but I don’t know how to accommodate the learning.
I would appreciate any insights/pointing to relevant literature/relevant code.