講座名稱：Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness

講座人：榮膺 教授

講座時間：12月13日14:00

講座地點：騰訊會議直播（ID:609 859 703）

講座人介紹：

榮鷹博士現任上海交通大學安泰經濟與管理學院教授。他于2010年回國執教于上海交通大學，此前在美國加州大學伯克利分校和里海大學從事博士后科研工作，并在上海交通大學和美國里海大學分別獲學士、碩士和博士學位。榮鷹教授主要研究領域為服務系統的運營優化、新興商業模型的運作、零售運營管理、供應鏈管理、數據驅動的優化模型、實證研究。研究成果發表在Management Science、Operations Research、Manufacturing & Service Operations Management、Production and Operations Management、Naval Research Logistics、IIE Transactions等國際學術刊物上。榮鷹教授是2015年度國家優秀青年科學基金和2020年度國家杰出青年科學基金獲得者并且多次獲得過國際獎項，其中包括兩度MSOM最佳論文獎，TSL最佳論文獎和INFORMS Energy, Natural Resources & Environment Young Researcher Prize。

講座內容：

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.

主辦單位：數學與統計學院