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Automatic ad format selection via contextual bandits

Authors:

Deepak AgarwalAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1587 - 1594

https://doi.org/10.1145/2505515.2514700

Published: 27 October 2013 Publication History

Abstract

Visual design plays an important role in online display advertising: changing the layout of an online ad can increase or decrease its effectiveness, measured in terms of click-through rate (CTR) or total revenue. The decision of which lay- out to use for an ad involves a trade-off: using a layout provides feedback about its effectiveness (exploration), but collecting that feedback requires sacrificing the immediate reward of using a layout we already know is effective (exploitation). To balance exploration with exploitation, we pose automatic layout selection as a contextual bandit problem. There are many bandit algorithms, each generating a policy which must be evaluated. It is impractical to test each policy on live traffic. However, we have found that offline replay (a.k.a. exploration scavenging) can be adapted to provide an accurate estimator for the performance of ad layout policies at Linkedin, using only historical data about the effectiveness of layouts. We describe the development of our offline replayer, and benchmark a number of common bandit algorithms.

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Cited By

Ghoorchian SKortukov EMaghsudi S(2024)Non-Stationary Linear Bandits With Dimensionality Reduction for Large-Scale Recommender SystemsIEEE Open Journal of Signal Processing10.1109/OJSP.2024.33864905(548-558)Online publication date: 2024
https://doi.org/10.1109/OJSP.2024.3386490
Brahmachari SLumbreras JTomamichel M(2024)Quantum contextual bandits and recommender systems for quantum dataQuantum Machine Intelligence10.1007/s42484-024-00189-66:2Online publication date: 12-Sep-2024
https://doi.org/10.1007/s42484-024-00189-6
Singh NSingh S(2024)A systematic literature review of solutions for cold start problemInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02359-y15:7(2818-2852)Online publication date: 14-May-2024
https://doi.org/10.1007/s13198-024-02359-y
Show More Cited By

Index Terms

Automatic ad format selection via contextual bandits
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Systems theory
2. Mathematics of computing
  1. Information theory

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Ghoorchian SKortukov EMaghsudi S(2024)Non-Stationary Linear Bandits With Dimensionality Reduction for Large-Scale Recommender SystemsIEEE Open Journal of Signal Processing10.1109/OJSP.2024.33864905(548-558)Online publication date: 2024
https://doi.org/10.1109/OJSP.2024.3386490
Brahmachari SLumbreras JTomamichel M(2024)Quantum contextual bandits and recommender systems for quantum dataQuantum Machine Intelligence10.1007/s42484-024-00189-66:2Online publication date: 12-Sep-2024
https://doi.org/10.1007/s42484-024-00189-6
Singh NSingh S(2024)A systematic literature review of solutions for cold start problemInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02359-y15:7(2818-2852)Online publication date: 14-May-2024
https://doi.org/10.1007/s13198-024-02359-y
Komiyama JImaizumi MOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)High-dimensional contextual bandit problem without sparsityProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668272(49416-49427)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668272
Cheng XMaghsudi S(2023)Collaborative Regret Minimization for Piecewise-Stationary Multi-Armed Bandit2023 31st European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO58844.2023.10289959(1385-1389)Online publication date: 4-Sep-2023
https://doi.org/10.23919/EUSIPCO58844.2023.10289959
Wang XWei MYao TSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Efficient Sparse Linear Bandits under High Dimensional DataProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599329(2431-2443)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599329
Mukherjee ATajer A(2023)SPRT-Based Efficient Best Arm Identification in Stochastic BanditsIEEE Journal on Selected Areas in Information Theory10.1109/JSAIT.2023.32889884(128-143)Online publication date: 2023
https://doi.org/10.1109/JSAIT.2023.3288988
Shmueli GTafti A(2023)How to “improve” prediction using behavior modificationInternational Journal of Forecasting10.1016/j.ijforecast.2022.07.00839:2(541-555)Online publication date: Apr-2023
https://doi.org/10.1016/j.ijforecast.2022.07.008
Chen QWang XJiang ZWu YLi HCui LSun X(2023)Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environmentsNeural Computing and Applications10.1007/s00521-023-08647-135:22(16193-16222)Online publication date: 20-May-2023
https://dl.acm.org/doi/10.1007/s00521-023-08647-1
Kruijswijk Jvan Emden RKaptein M(2023)Sequential Experimentation and LearningData Science for Entrepreneurship10.1007/978-3-031-19554-9_8(147-175)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-3-031-19554-9_8
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