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Budget Constrained Bidding in Keyword Auctions and Online Knapsack Problems
Zhou, Yunhong; Chakrabarty, Deeparnab; Lukose, Rajan
Keyword(s):Auctions and Online Knapsack Problems
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. To maximize revenue from sponsored search advertising, our bidding strategy can be oblivious (i.e., without knowledge) of other bidders' prices and/or click-through-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.
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