Discovery of Ranking Fraud Using Evidence Aggregation Approach for Mobile Apps
Keywords:
Mobile Apps, ranking fraud detection, evidence aggregation, historical ranking records, rating and reviewAbstract
Nowadays everyone is using smart phone. There is need of various applications to be installed on smart phone. To download application smart phone user have to visit Apps store such as Google Play Store, Apples store etc. When user visit play store then he or she is able to see the various applications list. This list is built on the basis of promotions and advertisements. User doesn't have knowledge about the application (i.e. Whether the application is useful or not.) So user looks at the list and downloads the applications mostly from first page of App Store. But sometimes it happens that the downloaded application won t work or is useless. That means it is fraud in mobile application list. To avoid this fraud, there is a need to develop an application which will list out the useful applications. To list the applications first we are going to find the active period of the application named as leading session. We are also investing the three types of evidences: 1. Ranking based evidence. 2. Rating based evidence. 3. Review based evidence. Using these three evidences finally we are calculating aggregation of these evidences. We Evaluate our application with real world data collected form play store for long time period.
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