Movie Recommendation System Using Machine Learning Algorithms

Abstract

Due to extravagant advantages of the big data, the recommendation systems are commonly used in different areas and technologies, including social networking, e-commerce and a vast range of web-based services. The film recommendation feature is very important in our lives because of its ability to provide enhanced entertainment for the user. Like this type of recommendation system, a selection of movies can be recommended to users based on their interest, or movie popularities. In today’s world, there is having many more personalized movie recommendation systems that are making use of movie databases which are freely accessible (e.g. Netflix, MovieLens and ErosNow), and enhanced performance and metrics. However, there is a fundamental issue which is still being ignored by recommendation system. Collaborative filtering is one of the main effective strategies for improvising the recommendation system but lacks with time complexity when working on huge data. So hereby in order to overcome the issue used a KNN (K Nearest Neighbor), Decision Tree and Logistic Regression algorithms which are mainly responsible for improvised performance and reduced time complexity of the Movie Recommendation System.