fastFM: A Library for Factorization Machines

@article{Bayer2016fastFMAL,
  title={fastFM: A Library for Factorization Machines},
  author={Immanuel Bayer},
  journal={Journal of Machine Learning Research},
  year={2016},
  volume={17},
  pages={184:1-184:5}
}
Factorization Machines (FM) are currently only used in a narrow range of applications and are not yet part of the standard machine learning toolbox, despite their great success in collaborative filtering and click-through rate prediction. However, Factorization Machines are a general model to deal with sparse and high dimensional features. Our Factorization Machine implementation (fastFM) provides easy access to many solvers and supports regression, classification and ranking tasks. Such an… CONTINUE READING
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