Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG
Abstract
We develop RecSim NG, a probabilistic platform that supports natural, concise specification and learning of models for multi-agent recommender systems simulation. RecSim NG is a scalable, modular,
differentiable simulator implemented in Edward2 and TensorFlow.
An extended version of this paper is available as arXiv:2103.08057.
Research Areas
