Synthetic Data-Based Simulators for Recommender Systems: A Survey. (arXiv:2206.11338v1 [cs.IR])

Web: http://arxiv.org/abs/2206.11338

This survey aims at providing a comprehensive overview of the recent trends
in the field of modeling and simulation (M&S) of interactions between users and
recommender systems and applications of the M&S to the performance improvement
of industrial recommender engines. We start with the motivation behind the
development of frameworks implementing the simulations -- simulators -- and the
usage of them for training and testing recommender systems of different types
(including Reinforcement Learning ones). Furthermore, we provide a new
consistent …

arxiv data recommender systems survey synthetic data systems