Movie Recommendation System using Machine Learning
Narendra Kumar Rao, Nagendra Panini Challa, S. Sreenivasa Chakravarthi, R Ranjana
Abstract
The recommendation engine filters information using specific algorithms and recommends high quality content to customers. It starts capturing more consumer behavior and based on that, recommends products that consumers can purchase. Three key strategies are used in our recommendation structures. One Demographic Filtering i.e. They offer general suggestions for each individual, based entirely on the film's image and genre. The system recommends similar films to all the users. if you consider that each person is of the same type, this method is considered very simple. The simple idea behind is that the movies which are more popular can be liked by the more people. The second method is content based filtering, which considers all the features like director, actors and movie related content and based on that the movies will be recommended. The third one is collaborative filtering, which implement the item based collaborative filtering and single value decomposition. The obtained results have showcased the proposed strategies with good accuracy.