A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation
This work provides a foundational resource for researchers and practitioners in visualization to improve recommendation systems by integrating perceptual knowledge, though it is incremental as it collates existing findings.
The paper addresses the gap between graphical perception research and visualization recommendation systems by reviewing 59 studies and creating a collated JSON dataset of knowledge. It demonstrates the dataset's utility in informing encoding decisions across three recommendation systems.
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.