Citing fastFM

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).

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

fastFM: A Library for Factorization Machines

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 has 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.

Supported Operating Systems

fastFM has a continuous integration / testing servers (Travis) for Linux (Ubuntu 14.04 LTS) and OS X Mavericks. Other OSs are not actively supported.

Usage

from fastFM import als
fm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)
fm.fit(X_train, y_train)
y_pred = fm.predict(X_test)

Tutorials and other information are available here. The C code is available as subrepository and provides a stand alone command line interface. If you still have questions after reading the documentation please open an issue at GitHub.

Task	Solver	Loss
Regression	als, mcmc, sgd	Square Loss
Classification	als, mcmc, sgd	Probit(Map), Probit, Sigmoid
Ranking	sgd	BPR

Supported solvers and tasks

Installation

binary install (64bit only)

pip install fastFM

source install

Please make sure, that Python and OS bit version agree, e.g. 32bit Python on 64bit OS won't work.

# Install cblas and python-dev header (Linux only).
# - cblas can be installed with libatlas-base-dev or libopenblas-dev (Ubuntu)
$ sudo apt-get install python-dev libopenblas-dev

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

# Enter the root directory
$ cd fastFM

# Install Python dependencies (Cython>=0.22, numpy, pandas, scipy, scikit-learn)
$ pip install -r ./requirements.txt

# Compile the C extension.
$ make                      # build with default python version (python)
$ PYTHON=python3 make       # build with custom python version (python3)

# Install fastFM
$ pip install .

Tests

The Python tests (pip install nose) can be run with: nosetests fastFM/fastFM/tests

Please refer to the fastFM-core README for instruction on how to run the C tests at fastFM/fastFM-core/src/tests.

Contribution

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

Contributions are very welcome! Since this project lives on GitHub we recommend 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.

Most information you need to setup your development environment can be learned by adapting the great instructions on https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md . Please ensure that your contribution conforms to the PEP8 Coding Style and includes unit tests where appropriate. More valuable guidelines that apply to fastFM can be found at http://scikit-learn.org/stable/developers/contributing.html#coding-guidelines .

Contributors

Project Name	Stars	Repos Using This	Packages Using This	Most Recent Commit	Total Releases	Latest Release	Open Issues	License	Language
Ad Papers	3,009			2 years ago			2	mit	Python
Papers on Computational Advertising
Xlearn	3,000	1	11	a year ago	10	December 04, 2018	191	apache-2.0	C++
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Sparrowrecsys	1,634			a year ago			21	apache-2.0	Python
A Deep Learning Recommender System
Fastfm	955	8		2 years ago	8	November 23, 2017	52	other	Python
fastFM: A Library for Factorization Machines
Lightctr	599			4 years ago			1	apache-2.0	C++
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
Daisyrec	496			2 months ago	15	August 14, 2022		mit	Python
This is the repository of our article published in RecSys 2020 "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" and of several follow-up studies.
Ytk Learn	349			a year ago			n,ull	mit	Java
Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax).
Attentional_factorization_machine	316			5 years ago			11		Python
TensorFlow Implementation of Attentional Factorization Machine
Polylearn	207			3 years ago			7	bsd-2-clause	Python
A library for factorization machines and polynomial networks for classification and regression in Python.
Rankfm	150			2 months ago	10	June 13, 2020	13	gpl-3.0	Python
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data

Fastfm