Content Based Movie Recommendation System Using Machine Learning Algorithm

A recommendation system grounded in user preferences can be applied to suggest various types of content such as web runners, books, pictures, television shows, and songs. The objective of a movie recommendation system is to recommend movies to individuals based on their interests, helping them save time searching the internet for movies amidst the overwhelming options available. A content-grounded recommendation system utilizes specific characteristics to determine suitable recommendations for users. By analyzing attributes present in previously liked movies, the system predicts which movies a user is likely to enjoy. Recommendation systems can utilize one or a combination of multiple attributes to generate recommendations. When designing a movie recommendation system, factors such as genre, ID, keywords, title, director, and actors are taken into account. This paper presents a movie recommender system based on cast, keywords, crew, and genres. A unified tag is created by combining these seven attributes, serving as the primary tag for the movie recommendation system, which guides the selection of recommended movies. This system aids users in saving time by providing personalized movie recommendations, eliminating the need to sift through countless options online. A content-based recommendation system is employed to identify items that are likely to be of interest to users. By leveraging a dataset, the system predicts user preferences based on various attributes.