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Ad click prediction: a view from the trenches

Authors:

H. Brendan McMahan,

Eugene Davydov,

Daniel Golovin,

Sharat Chikkerur,

Martin Wattenberg,

Arnar Mar Hrafnkelsson,

Jeremy KubicaAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1222 - 1230

https://doi.org/10.1145/2487575.2488200

Published: 11 August 2013 Publication History

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.

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Cited By

Hutchinson SAlizadeh M(2024)Safe Online Convex Optimization with First-Order Feedback2024 American Control Conference (ACC)10.23919/ACC60939.2024.10644318(1-7)Online publication date: 10-Jul-2024
https://doi.org/10.23919/ACC60939.2024.10644318
Xin FShen YLu C(2024)Application of a weighted ensemble forecasting method based on online learning in subseasonal forecast in the South ChinaGeoscience Letters10.1186/s40562-024-00319-911:1Online publication date: 30-Jan-2024
https://doi.org/10.1186/s40562-024-00319-9
Aggarwal GBadanidiyuru ABalseiro SBhawalkar KDeng YFeng ZGoel GLiaw CLu HMahdian MMao JMehta AMirrokni VLeme RPerlroth APiliouras GSchneider JSchvartzman ASivan BSpendlove KTeng YWang DZhang HZhao MZhu WZuo S(2024)Auto-Bidding and Auctions in Online Advertising: A SurveyACM SIGecom Exchanges10.1145/3699824.369983822:1(159-183)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3699824.3699838
Show More Cited By

Index Terms

Ad click prediction: a view from the trenches
1. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2013

1534 pages

ISBN:9781450321747

DOI:10.1145/2487575

Editors:
Rayid Ghani
University of Chicago
,
Ted E. Senator
SAIC
,
Paul Bradley
MethodCare, Inc.
,
Rajesh Parekh
Groupon
,
Jingrui He
Stevens Institute of Technology
,
General Chairs:
Robert L. Grossman
University of Chicago and Open Data Group
,
Ramasamy Uthurusamy
General Motors Corporation (retired)
,
Program Chairs:
Inderjit S. Dhillon
University of Texas
,
Yehuda Koren
Google

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 11 August 2013

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KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11 - 14, 2013

Illinois, Chicago, USA

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KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Hutchinson SAlizadeh M(2024)Safe Online Convex Optimization with First-Order Feedback2024 American Control Conference (ACC)10.23919/ACC60939.2024.10644318(1-7)Online publication date: 10-Jul-2024
https://doi.org/10.23919/ACC60939.2024.10644318
Xin FShen YLu C(2024)Application of a weighted ensemble forecasting method based on online learning in subseasonal forecast in the South ChinaGeoscience Letters10.1186/s40562-024-00319-911:1Online publication date: 30-Jan-2024
https://doi.org/10.1186/s40562-024-00319-9
Aggarwal GBadanidiyuru ABalseiro SBhawalkar KDeng YFeng ZGoel GLiaw CLu HMahdian MMao JMehta AMirrokni VLeme RPerlroth APiliouras GSchneider JSchvartzman ASivan BSpendlove KTeng YWang DZhang HZhao MZhu WZuo S(2024)Auto-Bidding and Auctions in Online Advertising: A SurveyACM SIGecom Exchanges10.1145/3699824.369983822:1(159-183)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3699824.3699838
Morita ITakahashi SKomo YNishimura SArikawa MMisue K(2024)Color Recommendations Based on Individual DifferencesProceedings of the 17th International Symposium on Visual Information Communication and Interaction10.1145/3678698.3678701(1-8)Online publication date: 11-Dec-2024
https://dl.acm.org/doi/10.1145/3678698.3678701
Al Jurdi WAbdo JDemerjian JMakhoul A(2024)Group Validation in Recommender Systems: Framework for Multi-layer Performance EvaluationACM Transactions on Recommender Systems10.1145/36408202:1(1-25)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3640820
Huang JZhang LWang JJiang SHuang DDing CXu L(2024)Utilizing Non-click Samples via Semi-supervised Learning for Conversion Rate PredictionProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688151(350-359)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688151
Rohde D(2024)Why the Shooting in the Dark Method Dominates Recommender Systems PracticeProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688029(847-849)Online publication date: 8-Oct-2024
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Pan JXue WWang XYu HLiu XQuan SQiu XLiu DXiao LJiang JBaeza-Yates RBonchi F(2024)Ads Recommendation in a Collapsed and Entangled WorldProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671607(5566-5577)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671607
Tang JGao HHe LKatariya SBaeza-Yates RBonchi F(2024)Multi-objective Learning to Rank by Model DistillationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671597(5783-5792)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671597
Lin ZPan JZhang SWang XXiao XHuang SXiao LJiang JBaeza-Yates RBonchi F(2024)Understanding the Ranking Loss for Recommendation with Sparse User FeedbackProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671565(5409-5418)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671565
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