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We present a case that the newly emerging field of synthetic data in the areaof recommender systems should prioritize `doing data right'. We consider thiscatchphrase to have two aspects: First, we should not repeat the mistakes ofthe past, and, second, we should explore the full scope of opportunitiespresented by synthetic data as we move into the future. We argue that explicitattention to dataset design and description will help to avoid past mistakeswith dataset bias and evaluation. In order to fully exploit the opportunitiesof synthetic data, we point out that researchers can investigate new areas suchas using data synthesize to support reproducibility by making data open, aswell as FAIR, and to push forward our understanding of data minimization.

Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems