Towards Regret Free Slot Allocation in Billboard Advertisement

TL;DR

The paper tackles regret minimization in billboard advertisement under a multi-advertiser setting, formalizing a discrete allocation problem where the provider pays per advertiser only if the delivered influence meets the demand, otherwise uncertainty induces unsatisfied or excessive regret. It introduces four heuristic solutions—EA, EAOE, EBOE, and EBTE—that balance influence delivery and cost, with targeted neighborhood-search strategies to refine allocations. Extensive experiments on real trajectory and billboard datasets demonstrate that the proposed methods reduce total regret and often reduce computation time compared with established baselines, aided by preprocessing and a three-way splitting of billboard slots. The work advances practical, scalable strategies for regret-aware billboard allocation and suggests directions for online/adaptive extensions and pruning techniques to handle dynamic slot and advertiser arrivals. Overall, the approaches provide actionable, efficient mechanisms for regret minimization in trajectory-informed billboard advertising with real-world impact for advertisers and influence providers alike.

Abstract

Creating and maximizing influence among the customers is one of the central goals of an advertiser, and hence, remains an active area of research in recent times. In this advertisement technique, the advertisers approach an influence provider for a specific number of views of their content on a payment basis. Now, if the influence provider can provide the required number of views or more, he will receive the full, else a partial payment. In the context of an influence provider, it is a loss for him if he offers more or less views. This is formalized as 'Regret', and naturally, in the context of the influence provider, the goal will be to minimize this quantity. In this paper, we solve this problem in the context of billboard advertisement and pose it as a discrete optimization problem. We propose four efficient solution approaches for this problem and analyze them to understand their time and space complexity. We implement all the solution methodologies with real-life datasets and compare the obtained results with the existing solution approaches from the literature. We observe that the proposed solutions lead to less regret while taking less computational time.

Figures (18)

• Figure 1: Effective Allocation Policy of the Billboard Slots for the Regret Minimization Problem
• Figure 2: Effective Advertiser driven One-by-One Billboard Slot Exchange Policy for the Regret Minimization Problem
• Figure 3: Effective Billboard Driven One-by-One Exchange Policy for the Regret Minimization Problem
• Figure 4: Effective Billboard Driven One-by-Two Exchange Policy for the Regret Minimization Problem
• Figure 5: Regret varying $\alpha$ when $\mathcal{I}^{ID} = 1\%, \mathcal{|A|} = 100$ (a, b, c, d, e), when $\mathcal{I}^{ID} = 2\%, \mathcal{|A|} = 50$ (f,g,h,i,j), when $\mathcal{I}^{ID} = 5\%, \mathcal{|A|} = 20$ (k,l,m,n,o) and when $\mathcal{I}^{ID} = 10\%, \mathcal{|A|} = 10$ (p, q, r, s, t)for Beach location type

Theorems & Definitions (11)

• definition thmcounterdefinition: Billboard Slot
• definition thmcounterdefinition: Trajectory Database
• definition thmcounterdefinition: Billboard Database
• definition thmcounterdefinition: Influence of Billboard Slots
• definition thmcounterdefinition: The Regret Model
• definition thmcounterdefinition: Total Regret Associated with an allocation
• definition thmcounterdefinition: Regret Minimization in Billboard Advertisement Problem
• definition thmcounterdefinition: Effective Split of Billboard Slots
• theorem thmcountertheorem
• definition thmcounterdefinition: Marginal Regret of a Billoard Slot