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fastFM - fastFM: A Library for Factorization Machines

  •    Python

The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citations of the software. If you publish scientific articles using fastFM, please cite the following article (bibtex entry citation.bib). This repository allows you to use Factorization Machines in Python (2.7 & 3.x) with the well known scikit-learn API. All performance critical code as been written in C and wrapped with Cython. fastFM provides stochastic gradient descent (SGD) and coordinate descent (CD) optimization routines as well as Markov Chain Monte Carlo (MCMC) for Bayesian inference. The solvers can be used for regression, classification and ranking problems. Detailed usage instructions can be found in the online documentation and on arXiv.

rsparse - Fast and accurate machine learning on sparse matrices - Factorization Machines, FTRL, Matrix factorizations

  •    R

rsparse is an R package for statistical learning on sparse data. Notably it implements many algorithms sparse matrix factorizations with a focus on applications for recommender systems. All of the algorithms benefit from OpenMP and most of them use BLAS. Package scales nicely to datasets with millions of rows and millions of columns.


  •    Matlab

For the sake of ease, a quick instruction is given for readers to reproduce the whole process on yelp-50k dataset. Note that the programs are testd on Linux(CentOS release 6.9), Python 2.7 from Anaconda 4.3.6. One dependent lib is bottleneck, you may install it with "pip install bottleneck".

flurs - :ocean: FluRS: A Python library for streaming recommendation algorithms

  •    Python

FluRS is a Python library for online item recommendation. The name indicates Flu-* (Flux, Fluid, Fluent) Recommender Systems which incrementally adapt to dynamic user-item interactions in a streaming environment. Note that repository takuti/stream-recommender uses FluRS v0.0.1 to write research papers.

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