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Beyond sharing datasets or simulations, we believe the Recommender Systems(RS) community should share Task Environments. In this work, we propose ahigh-level logical architecture that will help to reason about the corecomponents of a RS Task Environment, identify the differences betweenEnvironments, datasets and simulations; and most importantly, understand whatneeds to be shared about Environments to achieve reproducible experiments. Thework presents itself as valuable initial groundwork, open to discussion andextensions.

Towards Sharing Task Environments to Support Reproducible Evaluations of Interactive Recommender Systems