  Findings from graphical perception can guide visualization recommendation
algorithms in identifying effective visualization designs. However, existing
algorithms use knowledge from, at best, a few studies, limiting our
understanding of how complementary (or contradictory) graphical perception
results influence generated recommendations. In this paper, we present a
pipeline of applying a large body of graphical perception results to develop
new visualization recommendation algorithms and conduct an exploratory study to
investigate how results from graphical perception can alter the behavior of
downstream algorithms. Specifically, we model graphical perception results from
30 papers in Draco -- a framework to model visualization knowledge -- to
develop new recommendation algorithms. By analyzing Draco-generated algorithms,
we showcase the feasibility of our method to (1) identify gaps in existing
graphical perception literature informing recommendation algorithms, (2)
cluster papers by their preferred design rules and constraints, and (3)
investigate why certain studies can dominate Draco's recommendations, whereas
others may have little influence. Given our findings, we discuss the potential
for mutually reinforcing advancements in graphical perception and visualization
recommendation research.
