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.
This work-in-progress presents a new algorithm that leverages social network structure to rank designs and users in online design communities. The algorithm is based on the intuition that the importance of a design should depend on the rank of the users that created and promoted it, and the importance of a user should depend on the rank of the designs he creates and promotes in turn. The algorithm produces design rankings that are positively correlated with existing social metrics such as number of likes, but also allows designs with second-order social import to rise through the ranks. We demonstrate that the algorithm converges, and analyze the rankings it produces on both simulated and scraped social design networks.
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