Describing Patterns and Disruptions in Large Scale Mobile App Usage Data

@article{Canneyt2017DescribingPA,
  title={Describing Patterns and Disruptions in Large Scale Mobile App Usage Data},
  author={Steven Van Canneyt and Marc Bron and Andrew Haines and Mounia Lalmas},
  journal={Proceedings of the 26th International Conference on World Wide Web Companion},
  year={2017}
}
The advertising industry is seeking to use the unique data provided by the increasing usage of mobile devices and mobile applications (apps) to improve targeting and the experience with apps. As a consequence, understanding user behaviours with apps has gained increased interests from both academia and industry. In this paper we study user app engagement patterns and disruptions of those patterns in a data set unique in its scale and coverage of user activity. First, we provide a detailed… 
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12 References

Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns

An app usage prediction model that leverages three key everyday factors that affect app usage decisions, including intrinsic user app preferences and user historical patterns, and user activities and the environment as observed through sensor-based contextual signals is developed.

Identifying diverse usage behaviors of smartphone apps

This paper presents results on app usage at a national level using anonymized network measurements from a tier-1 cellular carrier in the U.S. and identifies traffic from distinct marketplace apps based on HTTP signatures and presents aggregate results on their spatial and temporal prevalence, locality, and correlation.

Characterizing Smartphone Usage Patterns from Millions of Android Users

This paper presents an empirical analysis of app usage behaviors collected from millions of users of Wandoujia, a leading An- droid app marketplace in China, and explores multiple aspects of such behavior data and presents interesting patterns ofapp usage.

Differentiating smartphone users by app usage

It is shown that it is possible to differentiate users via their set of used apps, their app signature, and this opens an entirely new discussion regarding privacy.

Diversity in smartphone usage

A comprehensive study of smartphone use finds that qualitative similarities exist among users that facilitate the task of learning user behavior and demonstrates the value of adapting to user behavior in the context of a mechanism to predict future energy drain.

Discovering different kinds of smartphone users through their application usage behaviors

This work analyzed one month of application usage from 106,762 Android users and discovered 382 distinct types of users based on their application usage behaviors, using the author's own two-step clustering and feature ranking selection approach.

Capturing mobile experience in the wild: a tale of two apps

A long term and large scale study of the experience of mobile users through two popular but contrasting applications in the wild, using a measurement framework and library, called Insight, deployed on these two applications that are available through Apple's App Store and Google's Android Market.

Fast app launching for mobile devices using predictive user context

FALCON uses contexts such as user location and temporal access patterns to predict app launches before they occur, and provides systems support for effective app-specific prelaunching, which can dramatically reduce perceived delay.

You Are What Apps You Use: Demographic Prediction Based on User's Apps

The predictability of user demographics based on the list of a user's apps and the effect of the training set size and the number of apps on the predictability are looked into and it is shown that both of these factors have a large impact on the prediction accuracy.

Models of user engagement

This paper provides initial insights into engagement patterns, allowing for a better understanding of the important characteristics of how users repeatedly interact with a service or group of services.