SMURFF - Scalable Matrix Factorization Framework

What is Bayesian Matrix Factorization

Matrix factorization is a common machine learning technique for recommender systems, like books for Amazon or movies for Netflix.

The idea of these methods is to approximate the user-movie rating matrix R as a product of two low-rank matrices U and V such that R ≈ U × V . In this way U and V are constructed from the known ratings in R, which is usually very sparsely filled. The recommendations can be made from the approximation U × V which is dense. If M × N is the dimension of R then U and V will have dimensions M × K and N × K.

Bayesian probabilistic matrix factorization (BPMF) has been proven to be more robust to data-overfitting compared to non-Bayesian matrix factorization.

What is SMURFF

SMURFF is a highly optimized and parallelized framework for Bayesian Matrix and Tensors Factorization. SMURFF supports multiple matrix factorization methods:

• BPMF, the basic version;
• Macau, adding support for high-dimensional side information to the factorization;
• GFA, doing Group Factor Anaysis.

Macau and BPMF can also perform tensor factorization.

Examples

Documentation is generated from Jupyter Notebooks. You can find the notebooks in docs/notebooks and the resulting documentation on smurff.readthedocs.io

Installation

Using conda:

conda install -c vanderaa smurff

Compile from source code: see INSTALL.rst

Contributors

• Jaak Simm (Macau C++ version, Cython wrapper, Macau MPI version, Tensor factorization)
• Tom Vander Aa (OpenMP optimized BPMF, Matrix Cofactorization and GFA, Code Reorg)
• Adam Arany (Probit noise model)
• Tom Haber (Original BPMF code)
• Andrei Gedich
• Ilya Pasechnikov

Acknowledgements

This work was partly funded by the European projects ExCAPE (http://excape-h2020.eu) and EXA2CT, and the Flemish Exaptation project.