Collaborative Filtering, Matrix Factorization, Recommender Systems, Gradient Descent, Neural Networks.
x = torch.cat((U_latent, V_latent), dim=1) # Concatenates U and V # Non-linear NN replaces matrix multiplication x = torch.sigmoid(self.fc1(x)) # Non-linear activation function R_hat = self.fc2(x) # Output layer to calculate reconstruction return R_hat def calculate_loss(self, R, R_hat): return