We contribute a method to automate parameter configurations for chart layouts by learning from human preferences. Existing charting tools usually determine the layout parameters using predefined heuristics, producing sub-optimal layouts. People can repeatedly adjust multiple parameters (e.g., chart size, gap) to achieve visually appealing layouts. However, this trial-and-error process is unsystematic and time-consuming, without a guarantee of improvement. To address this issue, we develop Layout Quality Quantifier (LQ2), a machine learning model that learns to score chart layouts from pairwise crowdsourcing data. Combined with optimization techniques, LQ2 recommends layout parameters that improve the charts' layout quality. We apply LQ2 on bar charts and conduct user studies to evaluate its effectiveness by examining the quality of layouts it produces. Results show that LQ2 can generate more visually appealing layouts than both laypeople and baselines. This work demonstrates the feasibility and usages of quantifying human preferences and aesthetics for chart layouts.

中文翻译：

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学习使用众包成对的比较自动化图表布局配置

我们提供了一种通过从人的偏好中学习来自动化图表布局的参数配置的方法。现有的制图工具通常使用预定义的试探法来确定布局参数，从而产生次优的布局。人们可以反复调整多个参数（例如，图表大小，间距），以获得视觉上吸引人的布局。但是，这种反复试验的过程是不系统且耗时的，无法保证得到改进。为了解决这个问题，我们开发了Layout Quality Quantifier（LQ2），这是一种机器学习模型，可以从成对的众包数据中学习对图表布局进行评分。结合优化技术，LQ2建议使用布局参数，以提高图表的布局质量。我们将LQ2应用于条形图，并进行用户研究以通过检查其产生的版面质量来评估其有效性。结果表明，与外行和基线相比，LQ2可以生成更具视觉吸引力的布局。这项工作演示了量化人类对于图表布局的喜好和美学的可行性和用法。