Optimal Delivery with Budget Constraint in E-Commerce Advertising

TL;DR

This paper presents a linear programming approach to optimize ad delivery in e-commerce platforms, balancing revenue and diverse advertising goals, validated through offline simulations on Alibaba.

Contribution

It introduces a novel LP-based method for simultaneous optimization of revenue and advertising objectives under constraints in e-commerce ad systems.

Findings

Effective improvement in campaign performance

Enhanced platform revenue

Validated through Alibaba offline simulations

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.