  Selecting appropriate visual encodings is critical to designing effective
visualization recommendation systems, yet few findings from graphical
perception are typically applied within these systems. We observe two
significant limitations in translating graphical perception knowledge into
actionable visualization recommendation rules/constraints: inconsistent
reporting of findings and a lack of shared data across studies. How can we
translate the graphical perception literature into a knowledge base for
visualization recommendation? We present a review of 59 papers that study user
perception and performance across ten visual analysis tasks. Through this
study, we contribute a JSON dataset that collates existing theoretical and
experimental knowledge and summarizes key study outcomes in graphical
perception. We illustrate how this dataset can inform automated encoding
decisions with three representative visualization recommendation systems. Based
on our findings, we highlight open challenges and opportunities for the
community in collating graphical perception knowledge for a range of
visualization recommendation scenarios.
