  Design sharing sites provide UI designers with a platform to share their
works and also an opportunity to get inspiration from others' designs. To
facilitate management and search of millions of UI design images, many design
sharing sites adopt collaborative tagging systems by distributing the work of
categorization to the community. However, designers often do not know how to
properly tag one design image with compact textual description, resulting in
unclear, incomplete, and inconsistent tags for uploaded examples which impede
retrieval, according to our empirical study and interview with four
professional designers. Based on a deep neural network, we introduce a novel
approach for encoding both the visual and textual information to recover the
missing tags for existing UI examples so that they can be more easily found by
text queries. We achieve 82.72% accuracy in the tag prediction. Through a
simulation test of 5 queries, our system on average returns hundreds more
results than the default Dribbble search, leading to better relatedness,
diversity and satisfaction.
