  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.
