In recommendation systems collaborative filtering use the rating of
item by user to calculate similarity between users and items. The state- of-art techniques for recommendation systems are based on latent factor
models, which aim to learn user and item embedding for rebuild interaction
matrix to predict user-item preferences.
Keywords—Collaborative Filtering, Neural Networks, Deep Learning,
Matrix Factorization, Implicit Feedback.
See our google colab notebooks.
https://drive.google.com/drive/folders/1JBP4hEAY36kJtajO6saegouLafd1zMYz?usp=sharing