# SMURFF: a High-Performance Framework for Matrix Factorization

## TL;DR

SMURFF is an open-source, high-performance framework that enables scalable Bayesian matrix factorization for large datasets, facilitating applications like compound-activity prediction with flexible implementation options.

## Contribution

The paper introduces SMURFF, a versatile framework that significantly improves the computational efficiency and usability of Bayesian matrix factorization methods.

## Key findings

- Successfully used for large-scale compound-activity prediction
- Supports deployment on supercomputers and personal devices
- Provides comprehensive documentation and examples

## 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.