Average-case analysis of off-line and on-line knapsack problems
Journal of Algorithms - Special issue on SODA '95 papers
Revenue maximization when bidders have budgets
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Dynamics of bid optimization in online advertisement auctions
Proceedings of the 16th international conference on World Wide Web
Optimal delivery of sponsored search advertisements subject to budget constraints
Proceedings of the 8th ACM conference on Electronic commerce
Budget constrained bidding in keyword auctions and online knapsack problems
Proceedings of the 17th international conference on World Wide Web
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Stochastic models for budget optimization in search-based advertising
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
A Knapsack-Based Approach to Bidding in Ad Auctions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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We model budget-constrained keyword bidding in sponsored search auctions as a stochastic multiple-choice knapsack problem (S-MCKP) and design an algorithm to solve S-MCKP and the corresponding bidding optimization problem. Our algorithm selects items online based on a threshold function which can be built/updated using historical data. Our algorithm achieved about 99% performance compared to the offline optimum when applied to a real bidding dataset. With synthetic dataset and iid item-sets, its performance ratio against the offline optimum converges to one empirically with increasing number of periods.