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- W3162942519 abstract "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 paired 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." @default.
- W3162942519 created "2021-05-24" @default.
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- W3162942519 date "2021-05-06" @default.
- W3162942519 modified "2023-10-01" @default.
- W3162942519 title "Learning to Automate Chart Layout Configurations Using Crowdsourced Paired Comparison" @default.
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- W3162942519 doi "https://doi.org/10.1145/3411764.3445179" @default.
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