WATCH OR NOT: MOVIE RATING PREDICTION USING MATRIX FACTORIZATION
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. Machine Learning, in the recent years, has become one of the most researched topics. With thousands of movies releasing each year, it is hard for people to choose what movie to watch. In this project, we propose a novel way to predict the rating for an upcoming movie based on released movies. We use Machine Learning algorithms to first group similar movies together into several groups. We use k-means algorithm for clustering. This is the first learning phase. Next we submit a movie for which rating is to be found. We classify the movie into one of the groups using the SVM classifier. Once it is classified into a group, the score is predicted for the movie using Matrix Factorization. By predicting the rating of an upcoming movie, the user will get an idea of how good the movie is going to be and can choose the movie he is going to watch more prudently. We also test our system for its accuracy and compare the result with existing system.