A Survey on Movie Recommendation System by using Machine Learning Algorithm

People’s desires, trends, and interests change as the world changes. Similarly, in the realm of cinema, individuals prefer to watch movies based on their interests. Many digital cinema service vendors have developed, and their aim is to keep every member entertained to grow their company and popularity. To develop their firm, the content provider should offer movies that their customers would enjoy, so that they will keep watching their upcoming films. Customers are likely to extend the web-based movie network operator application on a regular basis if this is done. The goal of this work is to create a Machine Learning algorithm based on XGBoost using collaborative filtering-based movie system that will suggest cinema to all users depending on their choices and evaluations. In order to achieve this, information screening is utilized to recommend films based on genres across movies, and a filter is applied to compute genre depending on the subscriber and offer film data. To boost performance, the suggested system employs the latest learning approach, XGBoost. The findings demonstrate that the proposed method is successful for movie suggestion, and it reduces Root Mean Square Error.