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2016 JMLR JMLR 2016

fastFM: A Library for Factorization Machines

Abstract

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. [abs] [ pdf ][ bib ] [ code ] [ webpage ] © JMLR 2016. (edit, beta)

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🐣 Hot Topic Early Bird — collaborative filtering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy

Authors

Immanuel Bayer

Topics

Machine Learning > Core Methods > Regression Data Science & Analytics > Applications > Recommender Systems Machine Learning > Learning Types > Supervised Learning

Keywords

collaborative filtering sparse feature factorization machine click-through rate prediction
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