Detection of False Ranking Apps Using Level Aggregation

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R. Jeeva, et. al.


Each platform of mobile devices has its app store which is the source for apps, games, movies, books, etc. The apps are categorized under predefined labels based on the rules formulated in the app store. The apps have been ranked based on the ratings, reviews, downloads, and no. of installs. It helps the user to download the top-ranked app in a specific category. That ranking of an app makes them think that it will work better than others in an effective way. The evidence aggregation of the above attributes has less variation that doesn’t reflect the current status of an app which influences the ranking. For that, the attributes that have been frequently changed due to developer and user actions to be collected for a specific category in top charts. The attributes include version, last updated date, features of an app and keywords will undergo an independent process that produces the following levels: 1. Version change level, 2. Keyword matching level and 3. Feature matching level. Each value of a level has to be consolidated and aggregated to produce the final ranking of apps in a specified category. The actual ranking has been compared with the obtained ranking to find the deviation value and the false ranked app in the app store.


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How to Cite
et. al., R. J. . (2021). Detection of False Ranking Apps Using Level Aggregation . Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 2235–2242.
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