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.
翻译:近年来,在线拍卖非常普遍。平台管理员正在努力完善其能够产生高利润的拍卖机制,同时保持公平的资源分配。随着计算机技术的进步和理论框架中的瓶颈,研究人员正在利用深层学习方法向在线拍卖设计转变方向。在本条中,我们总结了拍卖机制设计中采用的一些共同的深层次学习基础设施,并展示了这些结构是如何演变的。我们还讨论了研究人员如何应对大型和动态工业环境中的制约因素和关切。最后,我们指出了目前有待解决的关于未来方向的若干问题。