Name
Abstract | ||
|---|---|---|
Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. App usage behaviors reveal rich clues regarding one's personal attributes. It is possible to predict smartphone users' demographic attributes through their app usage behaviors. In this paper, we predict users' gender and income level on a large-scale dataset of app usage records from 10,000 Android users. More specifically, we first extracted features from app usage behaviors in terms of app, category, and app usage sequence. Then, we accessed the predictive ability of individual features and combinations of different features for gender and income level. We achieved an accuracy of 82.49%, precision of 82.01%, recall of 81.38% and F1 score of 0.82 for gender, with the best set of features. For income level (three classes), we achieved an accuracy of 69.71%, precision of 70.31%, recall of 70.38% and F1 score of 0.70.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3267305.3274175 | UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Singapore
Singapore
October, 2018 |
Keywords | Field | DocType |
App usage behaviors, smartphones, user attributes | F1 score,Android (operating system),Computer science,Human–computer interaction,Recall | Conference |
ISBN | Citations | PageRank |
978-1-4503-5966-5 | 1 | 0.34 |
References | Authors | |
7 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sha Zhao | 1 | 48 | 9.96 |
| Feng Xu | 2 | 448 | 69.80 |
| Zhiling Luo | 3 | 38 | 8.77 |
| Shijian Li | 4 | 1155 | 69.34 |
| Gang Pan | 5 | 1501 | 123.57 |