  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 value in 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. 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 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.
