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Sponsored search in E-commerce platforms such as Amazon, Taobao and Tmallprovides sellers an effective way to reach potential buyers with most relevantpurpose. In this paper, we study the auction mechanism optimization problem insponsored search on Alibaba's mobile E-commerce platform. Besides generatingrevenue, we are supposed to maintain an efficient marketplace with plenty ofquality users, guarantee a reasonable return on investment (ROI) foradvertisers, and meanwhile, facilitate a pleasant shopping experience for theusers. These requirements essentially pose a constrained optimization problem.Directly optimizing over auction parameters yields a discontinuous, non-convexproblem that denies effective solutions. One of our major contribution is apractical convex optimization formulation of the original problem. We devise anovel re-parametrization of auction mechanism with discrete sets ofrepresentative instances. To construct the optimization problem, we build anauction simulation system which estimates the resulted business indicators ofthe selected parameters by replaying the auctions recorded from real onlinerequests. We summarized the experiments on real search traffics to analyze theeffects of fidelity of auction simulation, the efficacy under variousconstraint targets and the influence of regularization. The experiment resultsshow that with proper entropy regularization, we are able to maximize revenuewhile constraining other business indicators within given ranges.

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