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The promise of visualization recommendation systems is that analysts will beautomatically provided with relevant and high-quality visualizations that willreduce the work of manual exploration or chart creation. However, littleresearch to date has focused on what analysts value in the design ofvisualization recommendations. We interviewed 18 analysts in the public healthsector and explored how they made sense of a popular in-domain dataset. inservice of generating visualizations to recommend to others. We also exploredhow they interacted with a corpus of both automatically- and manually-generatedvisualization recommendations, with the goal of uncovering how the designvalues of these analysts are reflected in current visualization recommendationsystems. We find that analysts champion simple charts with clear takeaways thatare nonetheless connected with existing semantic information or domainhypotheses. We conclude by recommending that visualization recommendationdesigners explore ways of integrating context and expectation into theirsystems.

Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health