• Tushar Kumar Tewary
• Mukundan.S
• Somasekar.S
• Indumaty.M

Abstract- The movies that are suggested in the websites are nowadays having more unrelated suggestions which are irrelevant to the user activity leading to unwanted or ignorant. The recommendation system incorporates content based filtering and collaborative filtering with matrix factorization playing a part in the arrangement and calculation of data for recommendation. In collaborative filtering the suggestions are based on many patterns which are similar to user’s views and the ratings and in content based filtering all the suggestions are based on the views and ratings of a single user’s activity. Now the acquired reviews and ratings are processed by filtering techniques for recommendation. While processing the dataset for training and testing, the missing or incomplete data will slower the process which can lead to inconsistencies in the recommendation. In this we are mainly concerned on recommendation of movies with less user activity for better suggestion of movies to entertain based on the user’s reviews and ratings.

Movie Recommendation System, Recommendation System, Recommender Engine

"Extensive Recommendation System For Movies Using User Ratings", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.440-442, April-2019, Available :http://www.jetir.org/papers/JETIR1904B67.pdf

"Extensive Recommendation System For Movies Using User Ratings", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp440-442, April-2019, Available at : http://www.jetir.org/papers/JETIR1904B67.pdf