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
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.
Topics & Concepts
Computer scienceModular designRecommender systemScalabilityProbabilistic logicDifferentiable functionArtificial intelligenceMachine learningProgramming languageDatabaseMathematical analysisMathematicsMulti-Agent Systems and NegotiationSimulation Techniques and ApplicationsReinforcement Learning in Robotics