2013 AdClickPredictionAViewfromtheTr

• (McMahan et al., 2013) ⇒ 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, and Jeremy Kubica. (2013). “Ad Click Prediction: A View from the Trenches.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2488200

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Cited By

• http://scholar.google.com/scholar?q=%222013%22+Ad+Click+Prediction%3A+A+View+from+the+Trenches
• http://dl.acm.org/citation.cfm?id=2487575.2488200&preflayout=flat#citedby

Quotes

Author Keywords

• Applications; data mining; large-scale learning; online advertising

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 AdClickPredictionAViewfromtheTr	D. Sculley Michael Young H. Brendan McMahan Gary Holt 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			Ad Click Prediction: A View from the Trenches				10.1145/2487575.2488200		2013