  Bayesian Matrix Factorization (BMF) is a powerful technique for recommender
systems because it produces good results and is relatively robust against
overfitting. Yet BMF is more computationally intensive and thus more
challenging to implement for large datasets. In this work we present SMURFF a
high-performance feature-rich framework to compose and construct different
Bayesian matrix-factorization methods. The framework has been successfully used
in to do large scale runs of compound-activity prediction. SMURFF is available
as open-source and can be used both on a supercomputer and on a desktop or
laptop machine. Documentation and several examples are provided as Jupyter
notebooks using SMURFF's high-level Python API.
