Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
Aug 13, 20208 views
A mobile app interface usually consists of a set of user interface,modules. How to properly design these user interface modules,is vital to achieving user satisfaction for a mobile app. However,,there are few methods to determine design variables for user interface modules except for relying on the judgment of designers.,Usually, a laborious post-processing step is necessary to verify the,key change of each design variable. Therefore, there is a only very,limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design,solutions as there are many modules. To this end, we introduce,FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning,approach. FEELER can help designers quantitatively measure the,preference score of different design solutions, aiming to facilitate,the designers to conveniently and quickly adjust user interface,module. We conducted extensive experimental evaluations on two,real-life datasets to demonstrate its applicability in real-life cases,of user interface module design in the Baidu App, which is one of,the most popular mobile apps in China.