Accordion: A Trainable Simulator for Long-Term Interactive Systems

Abstract

As machine learning methods are increasingly used in interactive systems it becomes common for user experiences to be the result of an ecosystem of machine learning models in aggregate. Simulation offers a way to deal with the resulting complexity by approximating the real system in a tractable and interpretable manner. Existing methods do not fully incorporate the interactions between user history, recommendation quality, and subsequent visits. We develop Accordion, a trainable simulator based on Poisson processes that can model visit patterns to an interactive system over time from large-scale data. New methods for training and simulation are developed and tested on two datasets of real world interactive systems. Accordion shows greater sensitivity to hyperparameter tuning and offline A/B testing than comparison methods, an important step in building realistic task-oriented simulators for recommendation.