Field-aware Factorization Machines in a Real-world Online Advertising System

@article{Juan2017FieldawareFM,
  title={Field-aware Factorization Machines in a Real-world Online Advertising System},
  author={Yu-Chin Juan and Damien Lefortier and Olivier Chapelle},
  journal={Proceedings of the 26th International Conference on World Wide Web Companion},
  year={2017}
}
Predicting user response is one of the core machine learning tasks in computational advertising. Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges. This paper presents some results from implementing this method in a production system that predicts click-through and conversion rates for display advertising and shows that this method it is not only effective to win challenges but is… 

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