Recommendation systems are an important part of suggesting items in streaming services. For streaming movie services like Netflix, recommendation systems are essential for helping users find new movies to enjoy. We propose a deep learning approach based on Deep autoencoders and Convolutional Neural Networks (CNN) to produce a collaborative filtering system which predicts movie ratings for a user based on a large database of ratings from other users. We use concept of deep learning to predict users’ ratings on new movies. The model uses model based Collaborative Filtering ,Single Value Decomposition(SVD) a matrix factorisation technique, which reduces the number of features of a dataset by reducing the space dimension from N-dimension to K-dimension (where K<N). Input is Movies and Ratings table which contains rating of users on various movies and the output will be the Top Recommended movies for the user predicted by the System.
SOFTWARES USED
• Visual Studio Code:
•MongoDB compass:
•Spyder-IDE
Backend Technologies:
• Python Version (2,7,2.8)
• Anaconda (Spyder Ide)
• pytorch
Requirements:
• OPERATING SYSTEM: windows 10
• PROCESSOR INTEL: CORE i3
• DISK STORAGE :3Gb