Issue No. 08 - Aug. (2017 vol. 29)
Didi Surian , Macquarie University, Sydney, NSW, Australia
Suranga Seneviratne , Data61, CSIRO, NSW, Australia
Aruna Seneviratne , University of New South Wales, NSW, Australia
Sanjay Chawla , Qatar Computing Research Institute (QCRI), HBKU, Doha, Qatar
An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce
FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+. Experiments on Google Play shows that FRAC+’s topics are more aligned with Google’s new categories and 0.35-1.10 percent game apps are detected to be miscategorized.
Google, Games, Australia, Data models, Support vector machines, Brain modeling, Mobile communication
D. Surian, S. Seneviratne, A. Seneviratne and S. Chawla, "App Miscategorization Detection: A Case Study on Google Play," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 8, pp. 1591-1604, 2017.