REVIEW 2 cited by
A Survey of Online Auction Mechanism Design Using Deep Learning Approaches
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 2 Pith papers
-
NGA: Non-autoregressive Generative Auction with Global Externalities for Advertising Systems
NGA is a non-autoregressive generative auction that models effects of adjacent organic content and computes rewards and payments in parallel, reporting gains in RPM, CTR, CVR, and latency over CGA.
-
EGA-V2: An End-to-end Generative Framework for Industrial Advertising
EGA-V2 unifies ad ranking, creative selection, allocation, and payment into one generative transformer, and reports offline revenue and CTR improvements over cascaded and generative baselines on Meituan data.
Discussion (0). Continue with ORCID to comment.