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Budget Constrained Bidding in Keyword Auctions and Online Knapsack Problems

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Internet and Network Economics (WINE 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5385))

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Abstract

We consider the budget-constrained bidding optimization problem for sponsored search auctions, and model it as an online (multiple-choice) knapsack problem. We design both deterministic and randomized algorithms for the online (multiple-choice) knapsack problems achieving a provably optimal competitive ratio. This translates back to fully automatic bidding strategies maximizing either profit or revenue for the budget-constrained advertiser. Our bidding strategy for revenue maximization is oblivious (i.e., without knowledge) of other bidders’ prices and/or clickthrough-rates for those positions. We evaluate our bidding algorithms using both synthetic data and real bidding data gathered manually, and also discuss a sniping heuristic that strictly improves bidding performance. With sniping and parameter tuning enabled, our bidding algorithms can achieve a performance ratio above 90% against the optimum by the omniscient bidder.

Work was done while the author were at HP Labs.

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Author information

Authors and Affiliations

  1. Rocket Fuel Inc, Redwood Shores, CA, USA

    Yunhong Zhou

  2. Georgia Tech, Atlanta, GA, USA

    Deeparnab Chakrabarty

  3. HP Labs, Palo Alto, CA, USA

    Rajan Lukose

Authors
  1. Yunhong Zhou
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  2. Deeparnab Chakrabarty
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  3. Rajan Lukose
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Editor information

Editors and Affiliations

  1. Computer Science Division, University of California at Berkeley, Berkeley, CA, USA

    Christos Papadimitriou

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Shuzhong Zhang

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhou, Y., Chakrabarty, D., Lukose, R. (2008). Budget Constrained Bidding in Keyword Auctions and Online Knapsack Problems. In: Papadimitriou, C., Zhang, S. (eds) Internet and Network Economics. WINE 2008. Lecture Notes in Computer Science, vol 5385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92185-1_63

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  • DOI: https://doi.org/10.1007/978-3-540-92185-1_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92184-4

  • Online ISBN: 978-3-540-92185-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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