The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justifyed by the number of citations of the software. If you publish scienctific articles using fastFM, pleease cite the following article (bibtex entry citation.bib).

Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)

This repository contains the source code for the fastFM C library and the stand-alone comand line interface (cli). In general we recommend to use fastFM with the Python interface.

fastFM-core/bin/bin/fastfm data/train_regression data/test_regression \
    --task regression   \
    --rng-seed 1234     \
    --init-var=0.11     \
    --n-iter=123        \
    --solver='mcmc'     \
    --rank 7            \
    --l2-reg=.22

Examples on how to use the other command line options options, including example data, can be found in fastFM-core/demo/Makefile. The demo/ folder contains examples showing how to use fastFM as C library.

Supported solvers and tasks

OS X: Library compiles on OSX, however console interface doesn't.

# Install cblas (Linux only).
$ sudo apt-get install libatlas-base-dev

# Clone the repro including submodules (or clone + `git submodule update --init --recursive`)
$ git clone --recursive https://github.com/ibayer/fastFM-core.git

# Build library
$ cd fastFM-core/; make;

# Build command line interface (currently this works on linux only)
$ make cli

OS X: Recommended way to manage dependencies is Homebrew package manager. If you have brew installed, dependencies can be installed by running command brew install glib gsl argp-standalone.

# The tests require the glib and gsl library (Linux, for OSX see above).
$ sudo apt-get install libglib2.0-dev libgsl0-dev

$ cd fastFM-core/src/tests

# Build the tests
$ make

# Run all tests
$ make check

• Star this repository: keeps contributors motivated
• Open a issue: report bugs or suggest improvements
• Fix errors in the documentation: small changes matter
• Contribute code

Contributions are very wellcome! Since this project lives on github we reommend to open a pull request (PR) for code contributions as early as possible. This is the fastest way to get feedback and allows Travis CI to run checks on your changes.

• check coding style (google) lang-format-3.5 -style=google -i <YOUR_FILE.c/h>
• static code analysis clang-tidy-3.5 -fix <YOUR_FILE.c/h> -- I.
• run valgrind memory check on sparse_test.c make mem_check
• run valgrind to check for errors valgrind -v ./a.out >& out

Contributors

• takuti
• altimin
• ibayer