mory22k / bayesfm

BayesFM using NumPy

My implementation of Bayesian Factorization Machines (BFM), as well as alternate least squares (ALS), using NumPy.

How to use

1. Install Python 3.12 via pyenv. See https://github.com/pyenv/pyenv for installation instructions.
2. Install poetry. See https://python-poetry.org/docs/#installation for installation instructions.
3. Run poetry shell to activate the virtual environment.
4. Run poetry install to install dependencies.
5. Run poetry run pytest to run the test scripts. FM models are trained using randomly generated data and their train RMSEs are visualized. The result figure will be saved in the test/out directory.

References

1. S. Rendle, Factorization Machines, in 2010 IEEE International Conference on Data Mining (2010), pp. 995–1000.
2. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme, Fast Context-Aware Recommendations with Factorization Machines, in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (2011), pp. 635–644.
3. S. Rendle, Factorization Machines with libFM, ACM Trans. Intell. Syst. Technol. 3, 1 (2012).

License

This project is licensed under the MIT license.