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fastFM : a library for factorization machines

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2016

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Journal of Machine Learning Research (JMLR). 2016, 17(1), pp. 6393-6397. ISSN 1532-4435. eISSN 1533-7928

Zusammenfassung

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 implementation simplifies the use of FM for a wide range of applications. Therefore, our implementation has the potential to improve understanding of the FM model and drive new development.

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004 Informatik

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Python, MCMC, matrix factorization, context-aware recommendation

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ISO 690BAYER, Immanuel, 2016. fastFM : a library for factorization machines. In: Journal of Machine Learning Research (JMLR). 2016, 17(1), pp. 6393-6397. ISSN 1532-4435. eISSN 1533-7928
BibTex
@article{Bayer2016fastF-37432,
  year={2016},
  title={fastFM : a library for factorization machines},
  number={1},
  volume={17},
  issn={1532-4435},
  journal={Journal of Machine Learning Research (JMLR)},
  pages={6393--6397},
  author={Bayer, Immanuel}
}
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