Matrix Factorization: Learning User and Movie Features from a Rating Matrix | NTU Hsuan-Tien Lin Machine Learning Techniques Lecture 15
The only input a recommender system gets is a user ID. Apply binary vector encoding to the ID and feed it to an N-d̃-M linear network with the tanh removed, and the hypothesis is h(x) = WᵀVx, which is the same as writing the rating matrix as R ≈ VᵀW. There are two ways to learn V and W: alternating least squares solves M+N linear regressions per round, and SGD uses one rating at a time to update the vectors of one user and one movie. The lecture closes by putting boosting, neural networks, RBF networks, matrix factorization, and k nearest neighbor on one map of extraction models.