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Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG

Martin Mladenov
Vihan Jain
Christopher Colby
Nicolas Mayoraz
Hubert Pham
Ivan Vendrov
RecSys '20: Fourteenth ACM Conference on Recommender Systems (2020), pp. 591-593


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.