User response modeling in recommender systems: a survey

Over the last several decades, recommender systems have become an integral part of both our daily lives and the research frontier at machine learning. In this survey, we explore various approaches to developing simulators for recommendation systems, especially for modeling the user response function. We consider simple probabilistic models, approaches based on generative adversarial networks, and full-scale simulators, and also review the datasets available for the research community.