j9九游会

上海治理论坛第549期

问题：Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness（客户选择和算法公正下的在线广告分配）

演讲人：；；；督淌
，
，，，上海交通大学安乐经济与治理学院

主持人：汪挺松教授
，
，，，j9九游会治理学院

时间：2025年6月23日（周一）
，
，，，上午9:00-12:00

所在：j9九游会校本部东区1号楼治理学院420聚会室

主理单位：j9九游会治理学院、j9九游会治理学院青年西席联谊会

演讲人简介：

；；；督淌谙秩紊虾＝煌ù笱е卫砜蒲迪抵魅巍⒔淌凇⒉┦可际
，
，，，天下着名专家
，
，，，主持国家自科青年基金A类（原国家优异青年基金项目）、国家自然科学基金重点项目等。。主要研究兴趣包括供应链治理和优化、数据剖析与商业决议等
，
，，，有多篇研究效果揭晓于Operations Research, Manufacturing & Service Operations Management等学术期刊上。。

演讲内容简介：

Advertising is a crucial revenue source for e-commerce platforms and a vital online marketing tool for their sellers. In this paper, we explore dynamic ad allocation with limited slots upon each customer arrival for an e-commerce platform, where customers follow a choice model when clicking 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 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 Fluid 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.

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