  We propose Macau, a powerful and flexible Bayesian factorization method for
heterogeneous data. Our model can factorize any set of entities and relations
that can be represented by a relational model, including tensors and also
multiple relations for each entity. Macau can also incorporate side
information, specifically entity and relation features, which are crucial for
predicting sparsely observed relations. Macau scales to millions of entity
instances, hundred millions of observations, and sparse entity features with
millions of dimensions. To achieve the scale up, we specially designed sampling
procedure for entity and relation features that relies primarily on noise
injection in linear regressions. We show performance and advanced features of
Macau in a set of experiments, including challenging drug-protein activity
prediction task.
