  Visualization recommendation (VisRec) systems provide users with suggestions
for potentially interesting and useful next steps during exploratory data
analysis. These recommendations are typically organized into categories based
on their analytical actions, i.e., operations employed to transition from the
current exploration state to a recommended visualization. However, despite the
emergence of a plethora of VisRec systems in recent work, the utility of the
categories employed by these systems in analytical workflows has not been
systematically investigated. Our paper explores the efficacy of recommendation
categories by formalizing a taxonomy of common categories and developing a
system, Frontier, that implements these categories. Using Frontier, we evaluate
workflow strategies adopted by users and how categories influence those
strategies. Participants found recommendations that add attributes to enhance
the current visualization and recommendations that filter to sub-populations to
be comparatively most useful during data exploration. Our findings pave the way
for next-generation VisRec systems that are adaptive and personalized via
carefully chosen, effective recommendation categories.
