English

Optimal Delivery with Budget Constraint in E-Commerce Advertising

Machine Learning 2019-10-09 v2 Artificial Intelligence Machine Learning

Abstract

Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search advertising systems) that assign ads to pages should satisfy objectives such as plenty of audience for branding advertisers, clicks or conversions for performance-based advertisers, at the same time try to maximize overall revenue of the platform. In this paper, we propose an approach based on linear programming subjects to constraints in order to optimize the revenue and improve different performance goals simultaneously. We have validated our algorithm by implementing an offline simulation system in Alibaba E-commerce platform and running the auctions from online requests which takes system performance, ranking and pricing schemas into account. We have also compared our algorithm with related work, and the results show that our algorithm can effectively improve campaign performance and revenue of the platform.

Keywords

Cite

@article{arxiv.1909.13221,
  title  = {Optimal Delivery with Budget Constraint in E-Commerce Advertising},
  author = {Chao Wei and Weiru Zhang and Shengjie Sun and Fei Li and Xiaonan Meng and Yi Hu and Hao Wang},
  journal= {arXiv preprint arXiv:1909.13221},
  year   = {2019}
}

Comments

13 pages, 5 figures

R2 v1 2026-06-23T11:29:17.612Z