Movie Recommendation System Using Deep Learning

Abstract

A recommendation system is being used by several streaming services to suggest new content that is relevant to currently available content. This project uses a movie dataset to train a deep learning model, which can then be used to predict which movies users will be recommended to watch next. In this project, a deep learning model was trained utilizing both the user ID and the movie ID as input features, and the ratings as the class label. A deep learning model built on the highest rated movies would suggest new movies to the same user. The suggested approach demonstrates the implementation of many functions covered in depth in the section on result analysis. To create a movie recommendation module, we have loaded a number of Python packages, including NumPy, SKLEARN, pandas, SNS, and many more. Accuracy parameters and the confusion matrix are computed to obtain the performance analysis. Performance metrics demonstrate that the suggested model outperforms cutting-edge methods in terms of performance