RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
This provides a simulation platform for researchers and practitioners working on multi-agent recommender systems, but it is incremental as it builds on existing simulation concepts with new probabilistic features.
The authors tackled the challenge of developing and training multi-agent recommender systems by introducing RecSim NG, a probabilistic simulation platform that enables transparent and configurable modeling of recommender ecosystems, resulting in a scalable and modular tool for researchers and practitioners.
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.