Advanced Content-based Movie Recommendation System

An Advanced Recommendation System is a system that provides accurate and efficient suggestions to the users for certain resources like books, medicines, movies, songs, etc., based on the specific data set containing the previously used resources by the users. The Movie recommendation system predicts what movies a user will enjoy based on the attributes of the previously watched movies by the user. This system leverages the historical user interactions with movies to predict the preferences and recommend the relevant content. Many critical attributes can be considered while designing a movie recommendation system like the genre of the movie, actors, crew, the director of the movie. The systems accuracy to recommend movies can be improved by a combination of two or more of these attributes.The Recommendation systems are particularly valuable for organizations that collect extensive customer data and aim to optimize their recommendations. They play a pivotal role in enhancing user experience by providing personalized suggestions for movies. The research work aims to build an advanced, stable and accurate recommendation system, which will be done by using a hybrid version of Content-based filtering and Cosine Similarity. Additionally, the system proposed is user friendly by providing the option to download the latest recommended movies dataset according to their popularity, vote count, likes and ratings. In this paper, we present an in-depth analysis of our proposed Movie Recommendation system, related work, proposed work, result analysis and conclusions.