Abstract
Display advertising has traditionally been sold via guaranteed contracts – a guaranteed contract is a deal between a publisher and an advertiser to allocate a certain number of impressions over a certain period, for a pre-specified price per impression. However, as spot markets for display ads, such as the RightMedia Exchange, have grown in prominence, the selection of advertisements to show on a given page is increasingly being chosen based on price, using an auction. As the number of participants in the exchange grows, the price of an impressions becomes a signal of its value. This correlation between price and value means that a seller implementing the contract through bidding should offer the contract buyer a range of prices, and not just the cheapest impressions necessary to fulfill its demand.
Implementing a contract using a range of prices, is akin to creating a mutual fund of advertising impressions, and requires randomized bidding. We characterize what allocations can be implemented with randomized bidding, namely those where the desired share obtained at each price is a non-increasing function of price. In addition, we provide a full characterization of when a set of campaigns are compatible and how to implement them with randomized bidding strategies.
A full version of this paper appears in [6].
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References
Babaioff, M., Hartline, J., Kleinberg, R.: Selling banner ads: Online algorithms with buyback. In: 4th Workshop on Ad Auctions (2008)
Boutilier, C., Parkes, D., Sandholm, T., Walsh, W.: Expressive banner ad auctions and model-based online optimization for clearing. In: National Conference on Artificial Intelligence, AAAI (2008)
Constantin, F., Feldman, J., Muthukrishnan, S., Pal, M.: Online ad slotting with cancellations. In: 4th Workshop on Ad Auctions (2008)
Csiszar, I.: Why least squares and maximum entropy? an axiomatic approach to interference for linear inverse problems. Annals of Statistics 19(4), 2032–2066 (1991)
Feige, U., Immorlica, N., Mirrokni, V.S., Nazerzadeh, H.: A combinatorial allocation mechanism with penalties for banner advertising. In: Proceedings of ACM WWW, pp. 169–178 (2008)
Ghosh, A., McAfee, P., Papineni, K., Vassilvitskii, S.: Bidding for representative allocations for display advertising. CoRR, abs/0910-0880 (2009)
McAfee, R.P., McMillan, J.: Auctions and bidding. Journal of Economic Literature 25(2), 699–738 (1987)
Milgrom, P.R.: A convergence theorem for competitive bidding with differential information. Econometrica 47(3), 679–688 (1979)
Parkes, D., Sandholm, T.: Optimize-and-dispatch architecture for expressive ad auctions. In: 1st Workshop on Ad Auctions (2005)
Sandholm, T.: Expressive commerce and its application to sourcing: How we conducted $35 billion of generalized combinatorial auctions 28(3), 45–58 (2007)
Wilson, R.: A bidding model of perfect competition. Rev. Econ. Stud. 44(3), 511–518 (1977)
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Ghosh, A., McAfee, P., Papineni, K., Vassilvitskii, S. (2009). Bidding for Representative Allocations for Display Advertising. In: Leonardi, S. (eds) Internet and Network Economics. WINE 2009. Lecture Notes in Computer Science, vol 5929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10841-9_20
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DOI: https://doi.org/10.1007/978-3-642-10841-9_20
Publisher Name: Springer, Berlin, Heidelberg
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