• Rupal Verma
• chaitali choudhary
• Ashok Behera

The key to the recommendation system is to predict user performance. Day –by-Day we see huge growth in E- Commerce and this continues growth in the E-commerce field gave the birth of a recommendation system. The recommendation system is done of different methods by using user- similarily, content-based method, collaborative filtering, Hybrid Models, and many more with various algorithms and accuracy.this essay critically examine how various algorithms perform and which of each algorithms performs with better accuracy between all of them .the most better accuracy c by this origination of recommendation system, there are various types of recommendation available in the market nowadays and each and every recommendation system works on distinct appearance like the interest of users, history of users, location of users and many more. In this process, we will generally discuss the movie recommendation system. Movie recommendation Engine will recommend movies to the users on the basis of their interest as well as rating and reviews by other users whose item interest is similar to eachother. This engine will work on the user similarity Model.

User-Similarity, Content-Based Method, Collaborative Filtering, Hybrid Models, Movie Recommendation Systems

"survey on Different Algorithms for Movie Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.b759-b763, August-2021, Available :http://www.jetir.org/papers/JETIR2108223.pdf

"survey on Different Algorithms for Movie Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppb759-b763, August-2021, Available at : http://www.jetir.org/papers/JETIR2108223.pdf