Building and training a neural recommender system

We are now going to build, compile, and train our model using our user-item ratings data. Specifically, we will use Keras to construct a customized neural network with embedded layers (one for users and one for items) and a lambda function that computes the dot product to build a working prototype of a neural network-based recommender system:

1. Let's get started using the following code:

# create custom model with user and item embeddings
dot <- function(
  embedding_dim,
  n_users,
  n_items,
  name = "dot"
) {
  keras_model_custom(name = name, function(self) {
    self$user_embedding <- layer_embedding(
        input_dim = n_users+1,
        output_dim = embedding_dim,
        name = "user_embedding")
    self$item_embedding <- layer_embedding(
        input_dim = n_items+1,
        output_dim = embedding_dim...