Budget constrained bidding in keyword auctions and online knapsack problems

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 (multiplechoice) 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 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.

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Yunhong Zhou, Deeparnab Chakrabarty, Rajan M. Lukose
• Keywords: Automatic Bidding Strategies, Bidding Algorithms, Bidding Optimization Problem, Internet Technology, WWW 2008