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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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
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