Name
Abstract | ||
|---|---|---|
Understanding smartphone users is fundamental for creating better smartphones, improving the smartphone usage experience, and generating generalizable and reproducible research. However, smartphone manufacturers and most of the mobile computing research community make a simplifying assumption that all smartphone users are similar or, at best, constitute a small number of user types, based on their behaviors. Manufacturers design phones for the broadest audience and hope they work for all users. Researchers mostly analyze data from smartphone-based user studies and report results without accounting for the many diff erent groups of people that make up the user base of smartphones. We challenge these elementary characterizations of smartphone users and show evidence of the existence of a much more diverse set of users. We 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 our own two-step clustering and feature ranking selection approach, and gave a meaningful label to the users in each cluster, such as Screen Checkers and Young Parents. Our results have profound implications on the reproducibility and reliability of mobile computing studies, design and development of applications, determination of which apps should be pre-installed on a smartphone and, in general, on the smartphone usage experience for diff erent types of users.
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Year | Venue | DocType |
|---|---|---|
2017 | GetMobile | Journal |
Volume | Issue | ISSN |
21 | 2 | 1559-1662 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sha Zhao | 1 | 48 | 9.96 |
| Julian Ramos | 2 | 198 | 11.63 |
| Jianrong Tao | 3 | 51 | 11.96 |
| Ziwen Jiang | 4 | 36 | 2.31 |
| Shijian Li | 5 | 1155 | 69.34 |
| Zhaohui Wu | 6 | 3121 | 246.32 |
| Gang Pan | 7 | 1501 | 123.57 |
| Anind Dey | 8 | 11484 | 959.91 |