Practical Constrained Optimization of Auction Mechanisms in E-Commerce Sponsored Search Advertising

TL;DR

This paper presents a convex optimization approach for tuning auction mechanisms in e-commerce sponsored search, balancing revenue, user experience, and advertiser ROI through a simulation-based framework.

Contribution

It introduces a novel convex reformulation of the auction optimization problem using a re-parametrization and simulation system, enabling effective constrained optimization.

Findings

Maximized revenue with entropy regularization.

Maintained business indicators within specified ranges.

Validated approach on real search traffic data.

Abstract

Sponsored search in E-commerce platforms such as Amazon, Taobao and Tmall provides sellers an effective way to reach potential buyers with most relevant purpose. In this paper, we study the auction mechanism optimization problem in sponsored search on Alibaba's mobile E-commerce platform. Besides generating revenue, we are supposed to maintain an efficient marketplace with plenty of quality users, guarantee a reasonable return on investment (ROI) for advertisers, and meanwhile, facilitate a pleasant shopping experience for the users. These requirements essentially pose a constrained optimization problem. Directly optimizing over auction parameters yields a discontinuous, non-convex problem that denies effective solutions. One of our major contribution is a practical convex optimization formulation of the original problem. We devise a novel re-parametrization of auction mechanism with discrete sets of representative instances. To construct the optimization problem, we build an auction simulation system which estimates the resulted business indicators of the selected parameters by replaying the auctions recorded from real online requests. We summarized the experiments on real search traffics to analyze the effects of fidelity of auction simulation, the efficacy under various constraint targets and the influence of regularization. The experiment results show that with proper entropy regularization, we are able to maximize revenue while constraining other business indicators within given ranges.