Ranking Fraud and Fake Reviews Detection for Mobile Apps

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Monali Zende
Prof.Aruna Gupta


Ranking fraud in the mobile App business propose to fraud activities which have an motivation behind, raising up the Apps in the
popular list. Presently days, number of shady means are utilized all the more frequently by application developers, such extending their Apps'
business or posting imposter App assessments, to give positioning distortion. There is a restricted research for avoiding ranking fraud. This
paper gives an entire idea of positioning deception and detects the Ranking fraud recognizable system for mobile Apps. This work is gathering
into three classifications. Initially is web ranking spam detection, second is online review spam recognition and last one is mobile application
recommendation. The Web ranking spam includes to any deliberate actions which convey to select Web pages an unjustifiable favorable
relevance or significance. Review spam is intended to give unfair view of a few products in order to impact the customers' view of the products
by specifically or indirectly influeating or damaging the product's reputation. In propose system we also remove the fake reviews from the
dataset using similarity measure algorithm and then detect the web rank spam. The experimental result shows that propose system save the time
as well as memory than the existing system.

Keywords: Mobile Apps, Ranking Fraud Detection, Evidence Aggregation, Historical Ranking Records, Rating and Review.


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