Collaborative Filtering, Matrix Factorization, Recommender Systems, Gradient Descent, Neural Networks.
model, however, the extra features/tuning that the complex model has gives it a bit of an edge in the end (beats a loss of 1.0 as opposed to the simple model which doesn’t). Run this yourself from the notebook! That wraps up this exploration of going from linear to non-linear matrix factorization,