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Bid optimization for broad match ad auctions
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International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents
Madrid, Spain
SESSION: Internet monetization/session: sponsored search table of contents
Pages 231-240  
Year of Publication: 2009
ISBN:978-1-60558-487-4
Authors
Eyal Even Dar  Google Research, New York, NY, USA
Vahab S. Mirrokni  Google Research, New York, NY, USA
S. Muthukrishnan  Google Research, New York, NY, USA
Yishay Mansour  Google Research and Tel-Aviv University, New York, NY, USA
Uri Nadav  Tel-Aviv University, Tel-Aviv, Israel
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Ad auctions in sponsored search support "broad match" that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits.

We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present a linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions.


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. Athey and G. Ellison. Position auctions with consumer search. Working Paper, Sept 2007.
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D. Chakrabarty, Y. Zhou, and R. Lukose. Budget constrained bidding in keyword auctions and online knapsack problems. 3rd Workshop on Sponsored Search Auctions (SSA), 2007.
 
6
B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second price auction selling billions of dollars worth of keywords. In Second workshop on sponsored search auctions, 2006.
7
8
 
9
D. Martin, J. Gehrke, and J. Halperin. Toward expressive and scalable sponsored search auctions. Proceedings of the 24th International Conference on Data Engineering, ICDE 2008.
10
 
11
S. Muthukrishnan, M. Pál, and Z. Svitkina. Stochastic models for budget optimization in search-based advertising. Proc. Workshop on Internet and Network Economics (WINE), 2007.
 
12
C. H. Papadimitriou and K. Steiglitz. Combinatorial optimization. Prentice Hall.
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14
 
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M. Sviridenko. A note on maximizing a submodular set function subject to knapsack constraint. Operations Research Letters 32 (2004), 41--43.
 
16
H. Varian. Position auctions. International Journal of Industrial Organization 25(6):1163--1178, December 2007.
 
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https://adcenter.microsoft.com/Default.aspx.
 
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https://clients.mamma.com/faq/bidsystem/faq_broad_matching.html.
 
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Yahoo! search marketing advertiser workbook. http://us.i1.yimg.com/us.yimg.com/i/us/ysm/misc/pdf/eworkbook.pdf.

Collaborative Colleagues:
Eyal Even Dar: colleagues
Vahab S. Mirrokni: colleagues
S. Muthukrishnan: colleagues
Yishay Mansour: colleagues
Uri Nadav: colleagues