Movie recommendation system using enhanced content-based filtering algorithm based on user demographic data

Now-a-days people are consuming content in form of movies, series, etc. for entertainment. In this modern era, people always look up to entertainment and in that process, they waste their time in searching for movies. Everyone wants to watch good films that have great content. It takes lot of time to search for a movie they like. Recommendation system comes into play in such situations. It helps to people by recommending movies. This paper develops a Movie Recommendation System to recommend movies based on different parameters. The principal objective of the project is to construct a movie recommendation framework to prescribe pictures to users. There are many algorithms that help to build a recommendation system. Here, the Content-based algorithm has been employed to recommend movies based on the similarity with other films by analyzing the content of the movie. To find the similarity, the cosine similarity method has been used. Here, the cosine similarity has been computed by using linear kernel, where the parameters are taken by the result of TF-IDF vectorization. Then the most similar movies are recommended.