The DOI matches its Crossref record; 20 of the 22 references it cites were found in a registry or an index.
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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“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]
Citations
Cites
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- 10.5555/2898607.2898800 · DOI
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Sorted by citations
Bayesian Reinforcement Learning: A Survey
Ghavamzadeh, M., Mannor, S., Pineau, J., & Tamar, A. (2015) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000049 · Cited by 347
An Information-Theoretic Analysis of Thompson SamplingPreprint
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Breaking the Curse of Horizon: Infinite-Horizon Off-Policy EstimationPreprint
Liu, Q., Li, L., Tang, Z., & Zhou, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12429 · Cited by 157
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Zhao, X., Gu, C., Zhang, H., Yang, X., Liu, X., Tang, J., & Liu, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16156 · Cited by 151
Online Evaluation for Information Retrieval
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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
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