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Reinforcement learning based recommender systems (RL-based RS) aim atlearning a good policy from a batch of collected data, by castingrecommendations to multi-step decision-making tasks. However, current RL-basedRS research commonly has a large reality gap. In this paper, we introduce thefirst open-source real-world dataset, RL4RS, hoping to replace the artificialdatasets and semi-simulated RS datasets previous studies used due to theresource limitation of the RL-based RS domain. Unlike academic RL research,RL-based RS suffers from the difficulties of being well-validated beforedeployment. We attempt to propose a new systematic evaluation framework,including evaluation of environment simulation, evaluation on environments,counterfactual policy evaluation, and evaluation on environments built fromtest set. In summary, the RL4RS (Reinforcement Learning for RecommenderSystems), a new resource with special concerns on the reality gaps, containstwo real-world datasets, data understanding tools, tuned simulationenvironments, related advanced RL baselines, batch RL baselines, andcounterfactual policy evaluation algorithms. The RL4RS suite can be found athttps://github.com/fuxiAIlab/RL4RS. In addition to the RL-based recommendersystems, we expect the resource to contribute to research in appliedreinforcement learning.

RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System