A Ranking Fraud Detection System For Mobile Apps

Abstract

Ranking fraud in the mobile App market refers to false or deceptive activities which have a reason of bumping up the Apps in the popularity list. Certainly, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. A ranking fraud detection system for mobile Apps was developed. Specifically, this ranking fraud happened in leading sessions and provided a method for mining leading sessions for each App from its historical ranking records and identified ranking based evidences, rating based evidences and review based evidences for detecting ranking fraud. Moreover, we proposed an optimization based aggregation method to integrate all the evidences for evaluating the credibility of leading sessions from mobile Apps. An unique perspective of this approach is that all the evidences can be modelled by statistical hypothesis tests, In this paper we want to propose more effective fraud evidences and analyze the latent relationship among rating, review and rankings. Moreover, we will extend our ranking fraud detection approach with other mobile App related services, such as mobile Apps recommendation, for enhancing user experience.