SMURFF: a High-Performance Framework for Matrix Factorization

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 hig...

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Veröffentlicht in:2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) S. 304 - 308
Hauptverfasser: Vander Aa, Tom, Chakroun, Imen, Ashby, Thomas J.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2019
Schlagworte:
Bayes methods
C++ languages
Collaborative filtering
Compounds
Machine learning
Motion pictures
multi-core
Prediction algorithms
Probabilistic matrix factorization algorithm
Sparse matrices
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Zusammenfassung: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.
DOI:10.1109/AICAS.2019.8771607