# SMURFF: a High-Performance Framework for Matrix Factorization

arXiv: https://arxiv.org/abs/1904.02514 · HTML: https://mlsanity.com/paper/1904.02514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Bayesian tensor factorization for predicting clinical outcomes using integrated human genetics evidence](https://mlsanity.com/paper/2207.12538/) (2022)

## Abstract (arXiv)

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.