Iterative and multi-level methods for Bayesian multirelational factorization with features. KU Leuven
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 ...