Dual Based DSP Bidding Strategy and its Application
This work addresses the challenge of maximizing revenue or performance for online advertisers in real-time bidding, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of optimizing ad selection and bid pricing for Demand Side Platforms in real-time bidding auctions under budget and ROI constraints, and demonstrated that their proposed dual-based strategy outperforms state-of-the-art methods in simulations and real applications.
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.