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

Jingbo Zhou, Zhenwei Tang, Min Zhao, Xiang Ge, Fuzheng Zhuang, Meng Zhou, Liming Zou, Chenglei Yang, Hui Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 only a very limited amount of design solutions that can be tested. It is time-consuming 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.

Original languageEnglish (US)
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages3346-3355
Number of pages10
ISBN (Electronic)9781450379984
DOIs
StatePublished - Aug 23 2020
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: Aug 23 2020Aug 27 2020

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Country/TerritoryUnited States
CityVirtual, Online
Period8/23/208/27/20

ASJC Scopus subject areas

  • Software
  • Information Systems

Keywords

  • collective learning
  • gaussian process
  • preference learning
  • user interface design
  • user interface exploration

Fingerprint

Dive into the research topics of 'Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning'. Together they form a unique fingerprint.

Cite this