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Automatic ad format selection via contextual bandits
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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A non-contextual multi-armed bandit model with layout identifiers has been applied previously to ad layout optimization.
“Such a model has been applied previously to ad layout optimization [19].”
From An Efficient Bandit Algorithm for Realtime Multivariate Optimization (2017) · cites this paper as [19]
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Metadata
- Authors
- Tang, L., Rosales, R., Singh, A., & Agarwal, D.
- Affiliations
- Florida International University (US)
- LinkedIn (United States) (US)
- Venue
- Proceedings of the 22nd ACM international conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2013-10-27
- Pages
- 1587-1594
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W2092092836
- Cited by
- 111
- References
- 22
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 8 · 2022: 5 · 2021: 15 · 2020: 17 · 2019: 15 · 2018: 8 · 2017: 7 · 2016: 9 · 2015: 11 · 2014: 4
- FWCI
- 13.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- contextual bandits, online display advertising, exploration-exploitation balance, click-through rate, advertising revenue, LinkedIn
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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