  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.
