Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison
This work addresses the problem of sub-optimal and time-consuming chart layout configuration for users of charting tools, aiming to automate the process based on human preferences.
This paper introduces Layout Quality Quantifier (LQ2), a machine learning model that learns to score chart layouts from crowdsourced pairwise comparisons. When combined with optimization techniques, LQ2 recommends layout parameters for bar charts that generate more visually appealing layouts than both laypeople and baseline methods.
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.