论文标题
可视化建议的建议：探索公共卫生的偏好和优先级
Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health
论文作者
论文摘要
可视化建议系统的承诺是，分析师将自动提供相关和高质量的可视化，以减少手动探索或图表创建的工作。但是，迄今为止，很少的研究集中在分析人员在可视化建议设计中的价值。我们采访了公共卫生部门的18位分析师，并探索了他们如何理解一个受欢迎的内域数据集。为了生成可视化，向他人推荐。我们还探索了他们如何与自动和手动生成的可视化建议的语料库进行交互，目的是发现这些分析师的设计值如何反映在当前的可视化建议系统中。我们发现，分析师倡导简单的图表，这些图表与现有的语义信息或域假设相关，但仍具有清晰的外卖图。我们通过建议可视化建议设计师探索将上下文和期望整合到系统中的方式来结束。
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.
