Demonstrating principled uncertainty modeling for recommender ecosystems with RecSim NG

22/09/2020

Demonstrating principled uncertainty modeling for recommender ecosystems with RecSim NG

Martin Mladenov, Chih-wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

Keywords: Probabilistic Programming, Reinforcement Learning, Latent Variable Models

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.

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