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arXiv · 1909.06133

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

Andrea Barraza-Urbina·Mathieu d'Aquin
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Abstract

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

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Andrea Barraza-Urbina, Mathieu d'Aquin. 2019-09-13. Towards Sharing Task Environments to Support Reproducible Evaluations of Interactive Recommender Systems. https://arxiv.org/abs/1909.06133

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