EdgeNet : Encoder-decoder generative Network for Auction Design in
  E-commerce Online Advertising
Guangyuan Shen Shengjie Sun Dehong Gao Libin Yang Yongping Shi Wei Ning
We present a new encoder-decoder generative network dubbed EdgeNet, which
introduces a novel encoder-decoder framework for data-driven auction design in
online e-commerce advertising. We break the neural auction paradigm of
Generalized-Second-Price(GSP), and improve the utilization efficiency of data
while ensuring the economic characteristics of the auction mechanism.
Specifically, EdgeNet introduces a transformer-based encoder to better capture
the mutual influence among different candidate advertisements. In contrast to
GSP based neural auction model, we design an autoregressive decoder to better
utilize the rich context information in online advertising auctions. EdgeNet is
conceptually simple and easy to extend to the existing end-to-end neural
auction framework. We validate the efficiency of EdgeNet on a wide range of
e-commercial advertising auction, demonstrating its potential in improving user
experience and platform revenue.