  A key task in design work is grasping the client's implicit tastes. Designers
often do this based on a set of examples from the client. However, recognizing
a common pattern among many intertwining variables such as color, texture, and
layout and synthesizing them into a composite preference can be challenging. In
this paper, we leverage the pattern recognition capability of computational
models to aid in this task. We offer a set of principles for computationally
learning personal style. The principles are manifested in PseudoClient, a deep
learning framework that learns a computational model for personal graphic
design style from only a handful of examples. In several experiments, we found
that PseudoClient achieves a 79.40% accuracy with only five positive and
negative examples, outperforming several alternative methods. Finally, we
discuss how PseudoClient can be utilized as a building block to support the
development of future design applications.
