  We propose RecSim, a configurable platform for authoring simulation
environments for recommender systems (RSs) that naturally supports sequential
interaction with users. RecSim allows the creation of new environments that
reflect particular aspects of user behavior and item structure at a level of
abstraction well-suited to pushing the limits of current reinforcement learning
(RL) and RS techniques in sequential interactive recommendation problems.
Environments can be easily configured that vary assumptions about: user
preferences and item familiarity; user latent state and its dynamics; and
choice models and other user response behavior. We outline how RecSim offers
value to RL and RS researchers and practitioners, and how it can serve as a
vehicle for academic-industrial collaboration.
