Iterative and multi-level methods for Bayesian multirelational factorization with features. KU Leuven
Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Numerical Analysis and Applied Mathematics (NUMA)
Many machine learning problems (classification, clustering, etc.) can be formulated as a factorization of an incompletely filled matrix where the goal is to predict the unknown values. These methods have been successful in large-scale recommender systems, like the Netflix challenge aimed at predicting movie preferences of users from 200 million movie ratings from half a million users. In particular, we are now generalizing the Bayesian ...