Practical Constrained Optimization of Auction Mechanisms in E-Commerce Sponsored Search Advertising
This work addresses the problem of optimizing auction mechanisms for e-commerce platforms like Alibaba, offering a practical solution to balance multiple business goals, though it is incremental in applying convex optimization to a specific domain.
The paper tackles the constrained optimization of auction mechanisms in Alibaba's sponsored search advertising, aiming to maximize revenue while maintaining marketplace efficiency, advertiser ROI, and user experience. The results show that with proper entropy regularization, revenue can be maximized while keeping other business indicators within specified ranges.
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.