  Visualization recommendation systems simplify exploratory data analysis (EDA)
and make understanding data more accessible to users of all skill levels by
automatically generating visualizations for users to explore. However, most
existing visualization recommendation systems focus on ranking all
visualizations into a single list or set of groups based on particular
attributes or encodings. This global ranking makes it difficult and
time-consuming for users to find the most interesting or relevant insights. To
address these limitations, we introduce a novel class of visualization
recommendation systems that automatically rank and recommend both groups of
related insights as well as the most important insights within each group. Our
proposed approach combines results from many different learning-based methods
to discover insights automatically. A key advantage is that this approach
generalizes to a wide variety of attribute types such as categorical,
numerical, and temporal, as well as complex non-trivial combinations of these
different attribute types. To evaluate the effectiveness of our approach, we
implemented a new insight-centric visualization recommendation system,
SpotLight, which generates and ranks annotated visualizations to explain each
insight. We conducted a user study with 12 participants and two datasets which
showed that users are able to quickly understand and find relevant insights in
unfamiliar data.
