  Item ranking systems support users in multi-criteria decision-making tasks.
Users need to trust rankings and ranking algorithms to reflect user preferences
nicely while avoiding systematic errors and biases. However, today only few
approaches help end users, model developers, and analysts to explain rankings.
We report on the study of explanation approaches from the perspectives of
recommender systems, explainable AI, and visualization research and propose the
first cross-domain design space for explainers of item rankings. In addition,
we leverage the descriptive power of the design space to characterize a)
existing explainers and b) three main user groups involved in ranking
explanation tasks. The generative power of the design space is a means for
future designers and developers to create more target-oriented solutions in
this only weakly exploited space.
