TY - GEN
T1 - Rico
T2 - 30th Annual ACM Symposium on User Interface Software and Technology, UIST 2017
AU - Deka, Biplab
AU - Huang, Zifeng
AU - Franzen, Chad
AU - Hibschman, Joshua
AU - Afergan, Daniel
AU - Li, Yang
AU - Nichols, Jeffrey
AU - Kumar, Ranjitha
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
PY - 2017/10/20
Y1 - 2017/10/20
N2 - Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from android apps at runtime. The Rico dataset contains design data from more than 9:7k android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 72k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports queryby-example search over UIs.
AB - Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from android apps at runtime. The Rico dataset contains design data from more than 9:7k android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 72k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports queryby-example search over UIs.
KW - App datasets
KW - Design mining
KW - Design search
KW - Mobile app design
UR - http://www.scopus.com/inward/record.url?scp=85041539433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041539433&partnerID=8YFLogxK
U2 - 10.1145/3126594.3126651
DO - 10.1145/3126594.3126651
M3 - Conference contribution
AN - SCOPUS:85041539433
T3 - UIST 2017 - Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology
SP - 845
EP - 854
BT - UIST 2017 - Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology
PB - Association for Computing Machinery
Y2 - 22 October 2017 through 25 October 2017
ER -