RecSim: A Configurable Simulation Platform for Recommender Systems

By Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier. 2019

215 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach (2020, methodology): “Some work has looked at the impact of RS policies on provider welfare (Singh & Joachims, 2018), on long-term user and provider metrics (e.g., using RL (Chen et al., 2018; Ie et al., 2019b) or assessing various other phenomena (Ribeiro et al., 2020; Celma, 2010)).”
An empirical investigation of the challenges of real-world reinforcement learning (2020, unknown): “We are inspired by a large range of real-world tasks, from control systems grounded in the physical world [Vecerik et al. 2019] to global-scale software systems interacting with billions of users [Covington et al. 2016; Ie et al. 2019].”
Know Your Action Set: Learning Action Relations for Reinforcement Learning (2022, methodology): “However, this effect is less apparent on RecSim because CPR requires only knowing the most common category, independent of user state.”
Reinforcement Learning based Recommender Systems: A Survey (2021, unknown): “Among those conducting a simulation study, SlateQ [80] introduces an open-source RLRS simulation environment, called RecSim [188], which gives the researcher lexibility to evaluate their algorithms in diferent settings.”