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Online allocation of display advertisements subject to advanced sales contracts
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising table of contents
Paris, France
Pages 69-77  
Year of Publication: 2009
ISBN:978-1-60558-671-7
Authors
Saeed Alaei  University of Maryland, College Park, MD
Esteban Arcaute  Stanford University, Stanford, CA
Samir Khuller  University of Maryland, College Park, MD
Wenjing Ma  Yahoo! Inc., Sunnyvale, CA
Azarakhsh Malekian  University of Maryland, College Park, MD
John Tomlin  Yahoo! Research, Sunnyvale, CA
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper we propose a utility model that accounts for both sales and branding advertisers. We first study the computational complexity of optimization problems related to both online and offline allocation of display advertisements. Next, we focus on a particular instance of the online allocation problem, and design a simple online algorithm with provable approximation guarantees. Our algorithm is near optimal as is shown by a matching lower bound. Finally, we report on experiments to establish actual case behavior on some real datasets, with encouraging results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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S. Fung, C. Poon, and F. Zheng. Improved randomized online scheduling of unit length intervals and jobs. In WAOA, 2008.
 
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R. Lewis and D. Reiley. Retail advertising works! measuring the effects of advertising on sales via a controlled experiment on yahoo! Technical report, Yahoo! Research, 2008.
 
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COIN-OR Foundation: http://www.coin-or.org.
 
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Collaborative Colleagues:
Saeed Alaei: colleagues
Esteban Arcaute: colleagues
Samir Khuller: colleagues
Wenjing Ma: colleagues
Azarakhsh Malekian: colleagues
John Tomlin: colleagues