Dual Based DSP Bidding Strategy and its Application

TL;DR

This paper introduces a novel dual-based DSP bidding strategy for RTB that maximizes revenue under constraints, formalized as an augmented MMKP, and demonstrates superior performance through simulations.

Contribution

It formalizes the DSP bidding problem as an augmented MMKP and proposes a dual-based solution, the first of its kind derived from second price auction assumptions.

Findings

Outperforms existing strategies in simulations

Applicable to multiple ads scenarios with various constraints

First dual-based framework derived from second price auction assumptions

Abstract

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.