Collaborative Filtering, Matrix Factorization, Recommender Systems, Gradient Descent, Neural Networks.
represents how far the reconstruction R_hat is from the real R. It needs to be differentiable so we can calculate how to update U and V every iteration of training. Let’s use the simple loss function: Mean Square Error or MSE. This simply takes R and R_hat and calculates the mean of the squared