Ad Click Prediction: a View from the Trenches
Venue
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2013)
Publication Year
2013
Authors
H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica
BibTeX
Abstract
Predicting ad click--through rates (CTR) is a massive-scale learning problem that
is central to the multi-billion dollar online advertising industry. We present a
selection of case studies and topics drawn from recent experiments in the setting
of a deployed CTR prediction system. These include improvements in the context of
traditional supervised learning based on an FTRL-Proximal online learning algorithm
(which has excellent sparsity and convergence properties) and the use of
per-coordinate learning rates. We also explore some of the challenges that arise in
a real-world system that may appear at first to be outside the domain of
traditional machine learning research. These include useful tricks for memory
savings, methods for assessing and visualizing performance, practical methods for
providing confidence estimates for predicted probabilities, calibration methods,
and methods for automated management of features. Finally, we also detail several
directions that did not turn out to be beneficial for us, despite promising results
elsewhere in the literature. The goal of this paper is to highlight the close
relationship between theoretical advances and practical engineering in this
industrial setting, and to show the depth of challenges that appear when applying
traditional machine learning methods in a complex dynamic system.