• Trupti Dhumal
• Tejal Dhoble
• Sanchita Chavan
• Pallavi Chandratre

Recommendation systems facilitate users notice and choose things (e.g., books, movies, restaurants) from the large variety out there on the online or in different electronic data sources. Given a large set of items and a description of the user &their needs, they present to the user a small set of the items that are well suited to the description. Similarly, a movie recommendation system provides a level of comfort and personalization that helps the user interact better with the system and watch movies that cater to his needs. In today’s digital world wherever there's associate degree endless sort of content to be consumed like books, videos, articles ,movies, etc., finding the content of one’s liking has become an irksome task. On the opposite hand digital content suppliers need to interact as several users on their service as doable for the most time. This is where recommender system comes into picture where the content providers recommend users the content according to the users’ liking.

Collaborative Filtering, Content Based Filtering, Movie Recommendation Psychological Test, Sentimental Analysis, Naïve Bayes Classifier

"MOVIE RECOMMENDATION SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.140-144, April-2019, Available :http://www.jetir.org/papers/JETIR1904A19.pdf

"MOVIE RECOMMENDATION SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp140-144, April-2019, Available at : http://www.jetir.org/papers/JETIR1904A19.pdf