MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces
This work addresses the lack of tools for designing and evaluating marketplace recommender systems, which is an incremental contribution to the field.
The paper tackles the challenge of developing recommender systems for marketplaces by introducing MARS-Gym, an open-source framework that enables researchers and engineers to build and evaluate reinforcement learning agents, demonstrated with baseline agents on the Trivago dataset.
Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In this context, we observed a lack of resources to design, train, and evaluate agents that learn by interacting within these environments. For this matter, we propose MARS-Gym, an open-source framework to empower researchers and engineers to quickly build and evaluate Reinforcement Learning agents for recommendations in marketplaces. MARS-Gym addresses the whole development pipeline: data processing, model design and optimization, and multi-sided evaluation. We also provide the implementation of a diverse set of baseline agents, with a metrics-driven analysis of them in the Trivago marketplace dataset, to illustrate how to conduct a holistic assessment using the available metrics of recommendation, off-policy estimation, and fairness. With MARS-Gym, we expect to bridge the gap between academic research and production systems, as well as to facilitate the design of new algorithms and applications.