2017/05/26 by Huahui Liu, Mingrui Zhu, Liu, Huahui +7
Computer Science · Decision Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Mobile Agent-Based Network Management #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.1705.09416
openalex publication_date 2017/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, RTB(Real Time Bidding) becomes a popular online advertisement trading method. During the auction, each DSP(Demand Side Platform) is supposed to evaluate current opportunity and respond with an ad and corresponding bid price. It's essential for DSP to find an optimal ad selection and bid price determination strategy which maximizes revenue or performance under budget and ROI(Return On Investment) constraints in P4P(Pay For Performance) or P4U(Pay For Usage) mode. We solve this problem by 1) formalizing the DSP problem as a constrained optimization problem, 2) proposing the augmented MMKP(Multi-choice Multi-dimensional Knapsack Problem) with general solution, 3) and demonstrating the DSP problem is a special case of the augmented MMKP and deriving specialized strategy. Our strategy is verified through simulation and outperforms state-of-the-art strategies in real application. To the best of our knowledge, our solution is the first dual based DSP bidding framework that is derived from strict second price auction assumption and generally applicable to the multiple ads scenario with various objectives and constraints.