  Ranking is a natural and ubiquitous way to facilitate decision-making in
various applications. However, different rankings are often used for the same
set of entities, with each ranking method placing emphasis on different
factors. These factors can also be multi-dimensional in nature, compounding the
problem. This complexity can make it challenging for an entity which is being
ranked to understand what they can do to improve their rankings, and to analyze
the effect of changes in various factors to their overall rank. In this paper,
we present RankBooster, a novel visual analytics system to help users
conveniently investigate ranking predictions. We take university rankings as an
example and focus on helping universities to better explore their rankings,
where they can compare themselves to their rivals in key areas as well as
overall. Novel visualizations are proposed to enable efficient analysis of
rankings, including a Scenario Analysis View to show a high-level summary of
different ranking scenarios, a Relationship View to visualize the influence of
each attribute on different indicators and a Rival View to compare the ranking
of a university and those of its rivals. A case study demonstrates the
usefulness and effectiveness of RankBooster in facilitating the visual analysis
of ranking predictions and helping users better understand their current
situation.
