Paper Info
Title
Deriving User Preferences of Mobile Apps from Their Management Activities
Abstract
App marketplaces host millions of mobile apps that are downloaded billions of times. Investigating how people manage mobile apps in their everyday lives creates a unique opportunity to understand the behavior and preferences of mobile device users, infer the quality of apps, and improve user experience. Existing literature provides very limited knowledge about app management activities, due to the lack of app usage data at scale. This article takes the initiative to analyze a very large app management log collected through a leading Android app marketplace. The dataset covers 5 months of detailed downloading, updating, and uninstallation activities, which involve 17 million anonymized users and 1 million apps. We present a surprising finding that the metrics commonly used to rank apps in app stores do not truly reflect the users’ real attitudes. We then identify behavioral patterns from the app management activities that more accurately indicate user preferences of an app even when no explicit rating is available. A systematic statistical analysis is designed to evaluate machine learning models that are trained to predict user preferences using these behavioral patterns, which features an inverse probability weighting method to correct the selection biases in the training process.
Year
DOI
Venue
2017
10.1145/3015462
ACM Trans. Inf. Syst.
Keywords
Field
DocType
Mobile apps,app management activities,behavior analysis
Behavioral pattern,Inverse probability weighting,Data mining,World Wide Web,User experience design,Computer science,Upload,Mobile device,Usage data,Mobile apps,Statistical analysis
Journal
Volume
Issue
ISSN
35
Issue-in-Progress
1046-8188
Citations 
PageRank 
References 
5
0.41
42
Authors
7
Name
Order
Citations
PageRank
Xuanzhe Liu168957.53
Wei Ai2534.44
Huoran Li3835.52
Jian Tang4132259.93
Gang Huang51223110.80
Feng Feng6814.06
Qiaozhu Mei74395207.09