Data-Driven Approaches to Modeling User Perception Towards Mobile User Interface

PhD Thesis Proposal Defence


Title: "Data-Driven Approaches to Modeling User Perception Towards Mobile
User Interface"

by

Mr. Ziming WU


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 
effective, this approach is resource-intensive.

In contrast, this thesis proposal 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 proposal by sketching the future
work on developing more supportive computational tools for mobile UI
design.


Date:                   Wednesday, 26 February 2020

Time:                   10:00am - 12:00noon

Zoom Meeting:           https://hkust.zoom.us/j/524005254

Committee Members:      Dr. Xiaojuan Ma (Supervisor)
                        Dr. Pedro Sander (Chairperson)
                        Prof. Chiew-Lan Tai
                        Dr. Sai-Kit Yeung (ISD)


**** ALL are Welcome ****