Document - Multi-store metadata-based supervised mobile App classification

2015

Conference article Open Access

Multi-store metadata-based supervised mobile App classification

Berardi G., Esuli A., Fagni T., Sebastiani F.

Mobile app classification

The mass adoption of smartphone and tablet devices has boosted the growth of the mobile applications market. Confronted with a huge number of choices, users may encounter difficulties in locating the applications that meet their needs. Sorting applications into a user-defined classification scheme would help the app discovery process. Systems for automatically classifying apps into such a classification scheme are thus sorely needed. Methods for automated app classification have been proposed that rely on tracking how the app is actually used on users' mobile devices; however, this approach can lead to privacy issues. We present a system for classifying mobile apps into user-defined classification schemes which instead leverages information publicly available from the online stores where the apps are marketed. We present experimental results obtained on a dataset of 5,993 apps manually classified under a classification scheme consisting of 50 classes. Our results indicate that automated app classification can be performed with good accuracy, at the same time preserving users' privacy.

Source: 30th Annual ACM Symposium on Applied Computing, pp. 585–588, Salamanca, ES, 13-17/04/2015

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@inproceedings{oai:it.cnr:prodotti:344465,
	title = {Multi-store metadata-based supervised mobile App classification},
	author = {Berardi G. and Esuli A. and Fagni T. and Sebastiani F.},
	doi = {10.1145/2695664.2695997},
	booktitle = {30th Annual ACM Symposium on Applied Computing, pp. 585–588, Salamanca, ES, 13-17/04/2015},
	year = {2015}
}