A Survey of Online Auction Mechanism Design Using Deep Learning Approaches

TL;DR

This survey reviews how deep learning techniques are increasingly used to design online auction mechanisms, highlighting architectures, challenges, and future research directions in dynamic industrial environments.

Contribution

It provides a comprehensive overview of deep learning approaches in online auction mechanism design, summarizing current architectures, challenges, and future research directions.

Findings

Deep learning architectures are evolving for auction design.

Researchers are addressing constraints in dynamic settings.

Several unresolved issues remain for future exploration.

Abstract

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.