SMURFF: a High-Performance Framework for Matrix Factorization
This provides a scalable tool for researchers and practitioners in recommender systems and bioinformatics, though it is incremental as it builds on existing BMF techniques.
The authors tackled the computational intensity of Bayesian Matrix Factorization (BMF) for large datasets by developing SMURFF, a high-performance framework that enables efficient composition and construction of BMF methods, successfully applied to large-scale compound-activity prediction.
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.