@inproceedings{3ce2dce14e0f4b738323bb61a85dc0f7,
title = "EdgeNet: Encoder decoder generative Network for Auction Design in E-commerce Online Advertising",
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 auto-regressive 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 auctions, demonstrating its potential in improving user experience and platform revenue.",
keywords = "Auction Design, Data-driven Auction, Online Advertising",
author = "Guangyuan Shen and Duanxiao Song and Shenjie Sun and Libin Yang and Dehong Gao and Zhen Wang and Yongping Shi and Wei Ning",
note = "Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 ; Conference date: 21-10-2023 Through 25-10-2023",
year = "2023",
month = oct,
day = "21",
doi = "10.1145/3583780.3615192",
language = "英语",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "4274--4278",
booktitle = "CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management",
}