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.
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.
Documentation is generated from Jupyter Notebooks. You can find the notebooks in docs/notebooks and the resulting documentation on smurff.readthedocs.io
Using conda:
conda install -c vanderaa smurffCompile from source code: see INSTALL.rst
- 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
This work was partly funded by the European projects ExCAPE (http://excape-h2020.eu) and EXA2CT, and the Flemish Exaptation project.