Hands-On Deep Learning with R

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 embeddingsdot <- 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( ...