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

TL;DR

This paper investigates how well user demographics can be predicted from their app usage data, revealing that certain demographics like gender are highly predictable, and analyzing factors affecting prediction accuracy.

Contribution

It extends previous work by predicting additional demographics, comparing dimensionality reduction methods, and analyzing the impact of app count and training size on predictability.

Findings

Gender prediction accuracy is 82.3%.

Income prediction accuracy is 60.3%.

Prediction accuracy varies with number of apps and training data size.

Abstract

Understanding the demographics of app users is crucial, for example, for app developers, who wish to target their advertisements more effectively. Our work addresses this need by studying the predictability of user demographics based on the list of a user's apps which is readily available to many app developers. We extend previous work on the problem on three frontiers: (1) We predict new demographics (age, race, and income) and analyze the most informative apps for four demographic attributes included in our analysis. The most predictable attribute is gender (82.3 % accuracy), whereas the hardest to predict is income (60.3 % accuracy). (2) We compare several dimensionality reduction methods for high-dimensional app data, finding out that an unsupervised method yields superior results compared to aggregating the apps at the app category level, but the best results are obtained simply by the raw list of apps. (3) We look into the effect of the training set size and the number of apps on the predictability and show that both of these factors have a large impact on the prediction accuracy. The predictability increases, or in other words, a user's privacy decreases, the more apps the user has used, but somewhat surprisingly, after 100 apps, the prediction accuracy starts to decrease.