SMURFF: a High-Performance Framework for Matrix Factorization
| Autor: | Tom Vander Aa, Thomas J. Ashby, Imen Chakroun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry 02 engineering and technology Overfitting Recommender system Supercomputer Machine learning computer.software_genre Matrix decomposition 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | AICAS 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) |
| DOI: | 10.1109/aicas.2019.8771607 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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