PROJECT TITLE :

App Miscategorization Detection: A Case Study on Google Play - 2017

ABSTRACT:

An ongoing challenge within the rapidly evolving app market ecosystem is to take care of the integrity of app categories. At the time of registration, app developers have to pick out, what they believe, is the foremost appropriate category for his or her apps. Besides the inherent ambiguity of selecting the right class, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of classes obtainable and probably reassign the apps. But, it has been observed that the mismatch between the outline of the app and also the class it belongs to, continues to persist. Although some common mechanisms (e.g., a grievance-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's based mostly 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 zero.35-1.ten percent game apps are detected to be miscategorized.