Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding

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.

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Yunhong Zhou, Victor Naroditskiy
• Keywords: Algorithm Selects Items, Bidding Optimization Problem, Internet Technology, Real Bidding Dataset, WWW 2008