Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Throughput-competitive on-line routing
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Prompt mechanism for ad placement over time
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
On the advice complexity of the knapsack problem
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Sponsored search auctions: an overview of research with emphasis on game theoretic aspects
Electronic Commerce Research
Budget smoothing for internet ad auctions: a game theoretic approach
Proceedings of the fourteenth ACM conference on Electronic commerce
Hi-index | 0.00 |
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.