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

Yuchin Juan, Damien Lefortier, Olivier Chapelle
2017 arXiv   pre-print
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
more » ... 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.
arXiv:1701.04099v3 fatcat:recjn5pp4zerbn6fis7dnar7ma