  Robust machine learning is an increasingly important topic that focuses on
developing models resilient to various forms of imperfect data. Due to the
pervasiveness of recommender systems in online technologies, researchers have
carried out several robustness studies focusing on data sparsity and profile
injection attacks. Instead, we propose a more holistic view of robustness for
recommender systems that encompasses multiple dimensions - robustness with
respect to sub-populations, transformations, distributional disparity, attack,
and data sparsity. While there are several libraries that allow users to
compare different recommender system models, there is no software library for
comprehensive robustness evaluation of recommender system models under
different scenarios. As our main contribution, we present a robustness
evaluation toolkit, Robustness Gym for RecSys (RGRecSys --
https://www.github.com/salesforce/RGRecSys), that allows us to quickly and
uniformly evaluate the robustness of recommender system models.
