Multi-objective Dynamic Auction Mechanism for Online Advertising

In online advertising, it is important to jointly consider multiple objectives, e.g. platform revenue and display quality. The existing work only considered multi-objective optimization in each single auction stage, which can be suboptimal in the setting with multiple stages in real online advertising. In this paper, we propose a dynamic auction mechanism which can make a tradeoff between revenue and quality across a series of multiple auction stages, to further improve the performance of the Pareto frontiers from the optimal static auction mechanism in each single stage. We prove that the proposed dynamic auction mechanism satisfies dynamic incentive properties. As the exact valuation distributions are usually unavailable in practice, we further propose a practical implementation of our multi-objective dynamic auction mechanism via data-driven methods and with guarantee of approximate dynamic incentive compatibility. Finally, we confirm our theoretical results and evaluate our practical implementation via empirical study on both synthetic data and real industrial data, and observe a significant improvement on both revenue and quality than the static auction mechanism baseline.