Mining smartphone data for app usage prediction and recommendations: A survey

• Published in: Pervasive and mobile computing Vol. 37; pp. 1 - 22
• Main Authors: Cao, Hong, Lin, Miao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2017
• Subjects: App prediction; App recommendation; Mobile apps; Smartphone data; App recommendation; Mobile apps; Smartphone data; App prediction
• ISSN: 1574-1192; 1873-1589
• DOI: 10.1016/j.pmcj.2017.01.007

Smartphones nowadays have become indispensable personal gadgets to support our activities in almost every aspect of our lives. Thanks to the tremendous advancement of smartphone technologies, platforms, as well as the enthusiasm of individual developers, numerous mobile applications (apps) have been created to serve a wide range of usage purposes, making our daily life more convenient. While these apps are used, data logs are typically generated and ambience context is recorded forming a rich data source of the smartphone users’ behaviors. In this paper, we survey existing studies on mining smartphone data for uncovering app usage patterns leveraging such a data source. Our discussions of the studies are organized according to two main research streams, namely app usage prediction and app recommendations alongside a few other related studies. Finally, we also present several challenges and opportunities in the emerging area of mining smartphone usage patterns.