Multi-store metadata-based supervised mobile app classification

Giacomo Berardi, Andrea Esuli, Tiziano Fagni, Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)


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. Copyright is held by the owner/author(s).

Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Print)9781450331968
Publication statusPublished - 13 Apr 2015
Event30th Annual ACM Symposium on Applied Computing, SAC 2015 - Salamanca, Spain
Duration: 13 Apr 201517 Apr 2015


Other30th Annual ACM Symposium on Applied Computing, SAC 2015


ASJC Scopus subject areas

  • Software

Cite this

Berardi, G., Esuli, A., Fagni, T., & Sebastiani, F. (2015). Multi-store metadata-based supervised mobile app classification. In Proceedings of the ACM Symposium on Applied Computing (Vol. 13-17-April-2015, pp. 585-588). Association for Computing Machinery.