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 also valuable in a real-world prediction system. We also discuss some specific challenges and solutions to reduce the training time, namely the use of an innovative seeding algorithm and a distributed learning mechanism.
@article{arxiv.1701.04099,
title = {Field-aware Factorization Machines in a Real-world Online Advertising System},
author = {Yuchin Juan and Damien Lefortier and Olivier Chapelle},
journal= {arXiv preprint arXiv:1701.04099},
year = {2017}
}