Calvin S.. Bao
University of Maryland, College Park, Maryland, United States
The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts \textit{value} in \revised{the design of} visualization recommendations. We interviewed 18 analysts in the public health sector and explored how they made sense of a popular in-domain dataset\footnote{National Health and Nutrition Examination Study 2013-2014~\cite{centers2013nhanes}.} in service of generating visualizations to recommend to others. We also explored how they interacted with a corpus of both automatically- and manually-generated visualization recommendations, with the goal of uncovering how the design values of these analysts are reflected in current visualization recommendation systems. We find that analysts \revised{champion} simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses. We conclude by recommending that visualization recommendation designers explore ways of integrating context and expectation into their systems.
https://dl.acm.org/doi/abs/10.1145/3491102.3501891
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)