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In this project ranking fraud in the movie business alludes to false or tricky exercises which have a motivation behind, knocking up the movies in the fame list. To be sure, it turns out to be more incessant for movie makers to utilize shady means, for example, expanding their movies' business or posting imposter movies evaluations, to confer positioning misrepresentation. While the significance of avoiding Ranking fraud has been generally perceived, there is constrained comprehension and examination here. This project gives a holistic perspective of positioning misrepresentation and propose a Ranking fraud identification framework for movies reviews. Identifying ranking fraud is actually to identify ranking fraud of movies reviews within such leading sessions. In this paper, an useful algorithm is used to discover the leading sessions based on the historical records and with the help of analysis of those records, it is proved that deceptive movies usually have different ranking patterns in each leading sessions as compared to the normal movies. Therefore it is illustrated from those ranking records that some fraud is taking place in movies market and to restrict those frauds, three main evidences are developed to detect such fraud. The proposed framework can be utilized in many recommendation tasks on the movies, including review suggestions, tag recommendations, expert finding, image recommendations, image annotations, etc. In this project, abbreviation based Review suggestion is also considered. In addition, search engine results comparison is also considered. Personalized recommendations are given importance. Previous search review words are also taken into review suggestion calculation.