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The recommendation systems become very important aspect in all areas like products ,movies , socials media and etc. Recommendation suggested from more overloaded information and analyze has to be done on massive amount of data. Apart from that media product like movie recommendation needs more process on previous and current data to suggest the user choice. In this paper we proposed a movie recommendation system based on the content and history based approach. This model starts the process by implementing of user similarity matrix. On the matrix results we applied the KNN to maps the user data to movie data based on the use choice. We also implemented the cosine similarity function between user choice to item data to find similarity and non similarity data outputs. This approach of movie recommendation system will do the process on history and content and produces the optimal results. [ABSTRACT FROM AUTHOR]