EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising

TL;DR

EdgeNet is a novel encoder-decoder neural network that enhances data utilization and economic properties in online advertising auctions by capturing mutual influences among ads and leveraging rich contextual information.

Contribution

The paper introduces EdgeNet, a transformer-based encoder and autoregressive decoder framework that improves auction efficiency and extends neural auction models for e-commerce advertising.

Findings

EdgeNet improves platform revenue in online advertising auctions.

It better captures mutual influences among ads.

The model enhances user experience in e-commerce platforms.

Abstract

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.