TY - GEN
T1 - Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning
AU - Zhou, Jingbo
AU - Tang, Zhenwei
AU - Zhao, Min
AU - Ge, Xiang
AU - Zhuang, Fuzheng
AU - Zhou, Meng
AU - Zou, Liming
AU - Yang, Chenglei
AU - Xiong, Hui
N1 - Funding Information: This research is supported in part by grants from the National Natural Science Foundation of China (Grant No.71531001,61972233,U1836206). Publisher Copyright: © 2020 ACM.
PY - 2020/8/23
Y1 - 2020/8/23
N2 - 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.
AB - 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.
KW - collective learning
KW - gaussian process
KW - preference learning
KW - user interface design
KW - user interface exploration
UR - http://www.scopus.com/inward/record.url?scp=85090402314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090402314&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3394486.3403387
DO - https://doi.org/10.1145/3394486.3403387
M3 - Conference contribution
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 3346
EP - 3355
BT - KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Y2 - 23 August 2020 through 27 August 2020
ER -