PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Berardi G., Esuli A., Fagni T., Sebastiani F. Multi-store metadata-based supervised mobile App classification. In: SAC'15 - 30th Annual ACM Symposium on Applied Computing (Salamanca, ES, 13-17 April 2015). Proceedings, pp. 585 - 588. ACM, 2015.
 
 
Abstract
(English)
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.
URL: http://dl.acm.org/citation.cfm?id=2695997&CFID=734381158&CFTOKEN=34893976
DOI: 10.1145/2695664.2695997
Subject Mobile app classification
I.2.6 ARTIFICIAL INTELLIGENCE. Learning


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional