SMURFF: a High-Performance Framework for Matrix Factorization
Abstract
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.
Cite
@article{arxiv.1904.02514,
title = {SMURFF: a High-Performance Framework for Matrix Factorization},
author = {Tom Vander Aa and Imen Chakroun and Thomas J. Ashby and Jaak Simm and Adam Arany and Yves Moreau and Thanh Le Van and José Felipe Golib Dzib and Jörg Wegner and Vladimir Chupakhin and Hugo Ceulemans and Roel Wuyts and Wilfried Verachtert},
journal= {arXiv preprint arXiv:1904.02514},
year = {2019}
}
Comments
European Commission Project: EPEEC - European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EC-H2020-80151)