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
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding
Proceedings of the 17th international conference on World Wide Web
A Knapsack Secretary Problem with Applications
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
An empirical analysis of return on investment maximization in sponsored search auctions
Proceedings of the 2nd International Workshop on Data Mining and Audience Intelligence for Advertising
Selling ad campaigns: online algorithms with cancellations
Proceedings of the 10th ACM conference on Electronic commerce
Stochastic models for budget optimization in search-based advertising
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Dynamic power allocation under arbitrary varying channels: an online approach
IEEE/ACM Transactions on Networking (TON)
Joint optimization of bid and budget allocation in sponsored search
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
QAVA: quota aware video adaptation
Proceedings of the 8th international conference on Emerging networking experiments and technologies
Real time bid optimization with smooth budget delivery in online advertising
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Dynamic dual adjustment of daily budgets and bids in sponsored search auctions
Decision Support Systems
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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 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.