Fairness Driven Slot Allocation Problem in Billboard Advertisement
Dildar Ali, Suman Banerjee, Shweta Jain, Yamuna Prasad
TL;DR
This work addresses the Fair Billboard Slot Allocation Problem (FBSA), allocating indivisible billboard slots to advertisers under a maximin fairness objective. It models trajectory-informed influence via $\mathcal{I}(\mathcal{S})=\sum_{t_j\in\mathcal{D}}\left(1-\prod_{b_i\in\mathbb{BS}}(1- Pr(b_i,t_j))\right)$, and leverages submodular valuation properties to derive a polynomial-time, $(\frac{1}{3})(1-\frac{1}{e}-\epsilon)$-approximate maximin fair allocation using a round-robin with thresholds framework; a refined submodular algorithm improves guarantees and runtime. The authors provide comprehensive complexity analyses and validate their approach on NYC and LA trajectory-billboard datasets, demonstrating balanced allocations that satisfy maximin fairness while preserving advertiser utility, with favorable efficiency and scalability relative to baseline methods. This work offers practical, provably fair slot allocation with real-world applicability for out-of-home advertising, and lays groundwork for extending fairness notions and guarantees in such allocation problems.
Abstract
In billboard advertisement, a number of digital billboards are owned by an influence provider, and several commercial houses (which we call advertisers) approach the influence provider for a specific number of views of their advertisement content on a payment basis. Though the billboard slot allocation problem has been studied in the literature, this problem still needs to be addressed from a fairness point of view. In this paper, we introduce the Fair Billboard Slot Allocation Problem, where the objective is to allocate a given set of billboard slots among a group of advertisers based on their demands fairly and efficiently. As fairness criteria, we consider the maximin fair share, which ensures that each advertiser will receive a subset of slots that maximizes the minimum share for all the advertisers. We have proposed a solution approach that generates an allocation and provides an approximate maximum fair share. The proposed methodology has been analyzed to understand its time and space requirements and a performance guarantee. It has been implemented with real-world trajectory and billboard datasets, and the results have been reported. The results show that the proposed approach leads to a balanced allocation by satisfying the maximin fairness criteria. At the same time, it maximizes the utility of advertisers.