Recommender systems are the primary interface connecting users to a wide variety of online content, and therefore must overcome a number of challenges across the user population in order to serve them equitably. To this end, in 2019 we released RecSim, a configurable platform for authoring simulation environments to facilitate the study of RL algorithms (the de facto standard ML approach for addressing sequential decision problems) in recommender systems. However, as the technology has progressed, it has become increasingly important to address the gap between simulation and real-world applications, ensuring that models are flexible and easily extendible, enabling probabilistic inference of user dynamics, and addressing computational efficiency. To address these issues, we recently released RecSim NG, the "Next Generation" of simulators for recommender systems research and development. RecSim NG is a response to a set of use cases that have emerged as important challenges in the application of simulation to real-world problems.

RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; an XLA-based vectorized execution model for running simulations on accelerated hardware; and tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem. Specifically, we present a collection of use cases that demonstrate how the functionality described above can help both researchers and practitioners easily develop and train novel algorithms for recommender systems. Please cite the paper if you use the code from this repository in your work. This is not an officially supported Google product.

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.