讲座名称：Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness
讲座地点：腾讯会议直播（ID:609 859 703）
荣鹰博士现任上海交通大学安泰经济与管理学院教授。他于2010年回国执教于上海交通大学，此前在美国加州大学伯克利分校和里海大学从事博士后科研工作，并在上海交通大学和美国里海大学分别获学士、硕士和博士学位。荣鹰教授主要研究领域为服务系统的运营优化、新兴商业模型的运作、零售运营管理、供应链管理、数据驱动的优化模型、实证研究。研究成果发表在Management Science、Operations Research、Manufacturing & Service Operations Management、Production and Operations Management、Naval Research Logistics、IIE Transactions等国际学术刊物上。荣鹰教授是2015年度国家优秀青年科学基金和2020年度国家杰出青年科学基金获得者并且多次获得过国际奖项，其中包括两度MSOM最佳论文奖，TSL最佳论文奖和INFORMS Energy, Natural Resources & Environment Young Researcher Prize。
Advertising is a major revenue source for e-commerce platforms and an important online marketing tool for e-commerce sellers. In this paper, we explore dynamic ad allocation with limited slots upon each customer arrival for e-commerce platforms when customers follow a choice model to click the ads. Motivated by the recent advocacy for the algorithmic fairness of online ad delivery, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. The original online ad-allocation problem is intractable, so we propose a novel, stochastic program framework (called two-stage target-debt, TTD) that first decides the click-through targets then devises an ad-allocation policy to satisfy these targets in the second stage. We show the asymptotic equivalence between the original problem, the relaxed click-through target optimization, and the fluid-approximation (FA) convex program. We also design a debt-weighted offer-set (DWO) algorithm and demonstrate that, as long as the problem size scales to infinity, this algorithm is (asymptotically) optimal under the optimal first-stage click-through target. Compared to the FA heuristic and its re-solving variants, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is highly desirable for the online advertising business in practice. Finally, our proposed model and algorithm help substantially improve the fairness of ad allocation for an online e-commerce platform without compromising its efficiency much.