Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage

By Matthias Böhmer, Brent J. Hecht, Johannes Schöning, A. Krüger, Gernot Bauer. Mobile HCI, 2011

686 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/e506b9db18cf939bedfffb5644f721e762d189d4

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Transportation

Citations in the corpus

A study of mobile device utilization (2015, background): “We also refer to the study in [9] on mobile applications usages, including what the popular applications are and how long each session (from opening to closing) normally last.”
A Survey of App Store Analysis for Software Engineering (2017, background): “[28] studied 4,100 Android users for app usage statistics.”
Targeted and depth-first exploration for systematic testing of android apps (2013, background): “SATG’s are useful for program understanding as they provide an at-a-glance view of the high-level app workﬂow.”
An in-situ study of mobile phone notifications (2014, background): “Given past work has shown that many mobile app interactions, in particular with social networking apps [6] and text”
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, background): “Most apps primarily communicate when active, and early studies suggested a limited number of apps are active simultaneously [14, 26].”
Large-scale assessment of mobile notifications (2014, background): “Beside, prior work categorization is not publicly available or do not include all apps in our dataset [2].”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “In addition, other information sources associated with U-I interactions are also exploited for recommender systems, such as the hunger status of a user when she rated a food menu [Ono et al. 2009], or the location where a user downloaded a mobile application [Böhmer et al. 2011].”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, methodology): “A more detailed description of the dataset and its collection procedure can be found in [6].”
Understanding the Long-Term Use of Smart Speaker Assistants (2018, methodology): “We now turn to directly comparing our data with findings from Böhmer et al.’s [6] analysis of mobile phone use.”