Recommender Systems from Learned Embeddings

We will use Movie ID and User ID to generate their corresponding embeddings. These embeddings are generated through the model training process along with other parameters. Once we have the embeddings, we build a K-Nearest Neighbor (KNN) model. Then whenever there is a user, we can get that user's embedding from our Neural Network model. We use this embedding to lookup in the KNN database and recommend top -- K movies to this user.