The machine learning-based movie recommender system to recommend movies to users based on their previous movie preferences.
The system utilizes a collaborative filtering approach, which analyzes the behavior of similar users and makes recommendations based on the movies they have watched and enjoyed. The system is developed using custom TensorFlow functions to enhance the performance of the model.
The model takes into account various factors such as user ratings to recommend movies that are most likely to be enjoyed by the user. The collaborative filtering algorithm is implemented using TensorFlow functions, which optimize the performance of the model, making it faster and more efficient.
Overall, the movie recommender system is a powerful tool that provides users with personalized recommendations for movies that they are likely to enjoy. The use of custom TensorFlow functions ensures that the system is optimized for high performance and accuracy. This system can be integrated into various platforms such as movie streaming services and can be improved with more data and feedback from users.