Movie Recommendation System Using Cosine Similarity

The recommendation system of today has made getting the stuff we need simple. The Recommendation systems are used to help people make decisions about movies to assist movie fans by making recommendations for movies to watch without the burden reducing the time-consuming process of choosing films. There are like different ways or we can say techniques and most of the OTT platforms and other movies sites depend upon the collaborative filtering technique (CB) which has some problems like cold start problem, scalability issue, etc. In this paper we aim to make movie recommendations based on the user's interests and preferences, we want to reduce the amount of human effort required. We developed a model based on a content-based approach and sentiment analysis. This system suggests movies by comparing examples supplied by the user to the contents of the movies. It does this without using any human-generated metadata and instead uses information about the director, cast, and genre of the movies as well as information about how positive or negative the reviews are plus also give additional details about the films you searched for. The rating of the film, its premiere date, cast, and genres are among the supplementary information.