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ABSTRACT
Despite the slowdown in the economy, advertisement revenue remains a significant source of income for many Internet-based organizations. Banner advertisements form a critical component of this income, accounting for 40 to 50 percent of the total revenue. There are considerable gains to be realized through the efficient scheduling of banner advertisements. This problem has been observed to be intractable via traditional optimization techniques, and has received only limited attention in the literature. This paper presents a procedure to generate advertisement schedules under the most commonly used advertisement pricing scheme---the CPM model. The solution approach is based on Lagrangean decomposition and is seen to provide extremely good advertisement schedules in a relatively short period of time, taking only a few hundred seconds of elapsed time on a 450 MHz PC compared to a few thousand seconds of CPU time on a workstation that other approaches need. Additionally, this approach can be incorporated into an actual implementation with minimal alterations and hence is of particular interest.
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CITED BY 6
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C. Narayanaswami , D. Coffman , M. C. Lee , Y. S. Moon , J. H. Han , H. K. Jang , S. McFaddin , Y. S. Paik , J. H. Kim , J. K Lee , J. W. Park , D. Soroker, Pervasive symbiotic advertising, Proceedings of the 9th workshop on Mobile computing systems and applications, February 25-26, 2008, Napa Valley, California
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Terry Payne , Ester David , Nicholas R. Jennings , Matthew Sharifi, Auction Mechanisms for Efficient Advertisement Selection on Public Displays, Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy, p.285-289, May 22, 2006
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REVIEW
"Mihai Caramihai : Reviewer"
Banner advertisements currently form a critical component of the income of Internet-based organizations, accounting for 40 to 50 percent of the total revenue. The efficient scheduling of banner advertisements cannot be analyzed via traditional eva
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