Optimal Delivery with Budget Constraint in E-Commerce Advertising
This work addresses ad serving optimization for e-commerce platforms, but it appears incremental as it builds on existing linear programming methods.
The paper tackles the problem of optimizing ad delivery in e-commerce platforms to maximize revenue while meeting various advertiser goals, and reports that their algorithm effectively improves campaign performance and platform revenue.
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.