THESIS
2020
xv, 111 pages : illustrations ; 30 cm
Abstract
Mobile User Interface (UI) serves as a major window where the communication between users
and mobile applications happens. It not only defines the look and feel of an app but also plays a
key role in creating good interactive experience with the installed functions and contents for users.
While designers strike to craft a good UI, a potential gap between designers’ intention and users’
perceived quality of the design might appear. Therefore, understanding how users perceive the
UI design, e.g., the perceived usability and aesthetics, is crucial for designers to reflect on and
reshape their products for better user experience. It requires designers to frequently elicit feedback
from target users and/or domain experts during the iterative app design process. Although deemed
effect...[
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Mobile User Interface (UI) serves as a major window where the communication between users
and mobile applications happens. It not only defines the look and feel of an app but also plays a
key role in creating good interactive experience with the installed functions and contents for users.
While designers strike to craft a good UI, a potential gap between designers’ intention and users’
perceived quality of the design might appear. Therefore, understanding how users perceive the
UI design, e.g., the perceived usability and aesthetics, is crucial for designers to reflect on and
reshape their products for better user experience. It requires designers to frequently elicit feedback
from target users and/or domain experts during the iterative app design process. Although deemed
effective, this approach is resource-intensive. In contrast, this thesis explores the use of data-driven
methods to model user perception towards mobile UI design and further support the generation of
more usable UI. We first propose a prediction model to infer the perceived brand personality of
mobile apps from their static UI pages. In particular, we compile a set of color-based, texture-based,
and organization-based visual descriptors of UI pages and demonstrate their promising predictive
power with a non-linear prediction model on a collected dataset. The results can benefit designers
by highlighting contributing graphical factors to brand personality creation. Next, to analyze the
dynamic UI changes, i.e., mobile UI animation, we introduce a two-stream deep neural network
to model the user engagement with UI animation, which shows a reasonable accuracy. Based on
the features encoded by the model, we further derive the potential design issues of animation to
inform design improvement. We develop a prototype AniLens and evaluate it with professional
designers. Finally, we investigate how computational powers can aid designers in generating more
user-friendly mobile UI. We leverage online curation data to generate the perceived semantics of
color filters. Our results indicate that the mobile UI of color filter applications incorporated with the
derived semantics which is in line with users’ consensus, can achieve better user experience. In all,
we demonstrate the reasonable effectiveness of our proposed data-driven methods in modeling user
perception towards mobile UI and also provide insight into how they can be leveraged to facilitate
UI generation. At the end, we conclude the thesis by sketching the future work on developing more
supportive computational tools for mobile UI design.
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