RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Abstract
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; 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.
A short version of this paper was published at RecSys 2020.
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; 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.
A short version of this paper was published at RecSys 2020.
Research Areas
