App Miscategorization Detection: A Case Study on Google Play

Didi Surian, Suranga Seneviratne, Aruna Seneviratne, Sanjay Chawla

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish
Article number7885558
Pages (from-to)1591-1604
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number8
StatePublished - Aug 2017
Externally publishedYes


  • App categorization
  • app market
  • miscategorization detection
  • mixture model
  • von-mises fisher distribution


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