  Synthetic data and simulators have the potential to markedly improve the
performance and robustness of recommendation systems. These approaches have
already had a beneficial impact in other machine-learning driven fields. We
identify and discuss a key trade-off between data fidelity and privacy in the
past work on synthetic data and simulators for recommendation systems. For the
important use case of predicting algorithm rankings on real data from synthetic
data, we provide motivation and current successes versus limitations. Finally
we outline a number of exciting future directions for recommendation systems
that we believe deserve further attention and work, including mixing real and
synthetic data, feedback in dataset generation, robust simulations, and
privacy-preserving methods.
