Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness

Xiaolong Li (), Ying Rong (), Renyu Zhang () and Huan Zheng ()
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Xiaolong Li: Institute of Operations Research and Analytics, National University of Singapore, Singapore 117602
Ying Rong: Antai College of Economics and Management, Data-Driven Management Decision-Making Lab, Shanghai Jiao Tong University, Shanghai 200030, China
Renyu Zhang: CUHK Business School, The Chinese University of Hong Kong, Hong Kong, China
Huan Zheng: Antai College of Economics and Management, Data-Driven Management Decision-Making Lab, Shanghai Jiao Tong University, Shanghai 200030, China

Management Science, 2025, vol. 71, issue 1, 825-843

Abstract: 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’s 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 ) that first decides the click-through targets and 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 ( Fluid ) convex program. We also design a debt-weighted offer-set 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 with the Fluid heuristic and its resolving 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 significantly compromising efficiency.

Keywords: online advertising platform; assortment optimization; algorithmic fairness; online convex optimization; mean reverting (search for similar items in EconPapers)
Date: 2025
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