  The development of recommender systems that optimize multi-turn interaction
with users, and model the interactions of different agents (e.g., users,
content providers, vendors) in the recommender ecosystem have drawn increasing
attention in recent years. Developing and training models and algorithms for
such recommenders can be especially difficult using static datasets, which
often fail to offer the types of counterfactual predictions needed to evaluate
policies over extended horizons. To address this, we develop RecSim NG, a
probabilistic platform for the simulation of multi-agent recommender systems.
RecSim NG is a scalable, modular, differentiable simulator implemented in
Edward2 and TensorFlow. It offers: a powerful, general probabilistic
programming language for agent-behavior specification; tools for probabilistic
inference and latent-variable model learning, backed by automatic
differentiation and tracing; and a TensorFlow-based runtime for running
simulations on accelerated hardware. We describe RecSim NG and illustrate how
it can be used to create transparent, configurable, end-to-end models of a
recommender ecosystem, complemented by a small set of simple use cases that
demonstrate how RecSim NG can help both researchers and practitioners easily
develop and train novel algorithms for recommender systems.
