A Survey of Online Auction Mechanism Design Using Deep Learning Approaches
It provides an overview for researchers and practitioners in online auctions, but is incremental as it surveys existing approaches without introducing new methods.
This survey summarizes deep learning infrastructures used in online auction mechanism design, discussing their evolution and how researchers address constraints in dynamic industrial settings, while pointing out unresolved issues for future work.
Online auction has been very widespread in the recent years. Platform administrators are working hard to refine their auction mechanisms that will generate high profits while maintaining a fair resource allocation. With the advancement of computing technology and the bottleneck in theoretical frameworks, researchers are shifting gears towards online auction designs using deep learning approaches. In this article, we summarized some common deep learning infrastructures adopted in auction mechanism designs and showed how these architectures are evolving. We also discussed how researchers are tackling with the constraints and concerns in the large and dynamic industrial settings. Finally, we pointed out several currently unresolved issues for future directions.