Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

TL;DR

FEELER is a collective learning framework that enables fast, intelligent exploration of user interface module designs in mobile apps, helping designers optimize interfaces efficiently.

Contribution

The paper introduces FEELER, a novel collective machine learning approach for rapid and quantitative exploration of UI design solutions, reducing reliance on manual judgment.

Findings

FEELER effectively predicts preference scores for UI designs.

Experimental results show FEELER accelerates UI design process.

Application to Baidu App demonstrates practical utility.

Abstract

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.