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.

本研究介绍了一种名为 EdgeNet 的新型编码器 - 解码器生成神经网络，其引入了一种新颖的编码器 - 解码器框架，用于在线电子商务广告中数据驱动的拍卖设计，通过破坏以广义第二价格为基础的神经拍卖模型，提高数据利用效率，并改进了拍卖机制的经济特征。