Balanced Popularity in Multi-Product Billboard Advertisement

2025-10-23

AI Summary

This paper addresses the billboard slot selection problem in multi-product advertising, aiming to maximize aggregate influence under two constraints: a total budget constraint (i.e., cost per product must not exceed its allocated budget) and a fairness constraint (i.e., the influence difference between any two products must be bounded by a given threshold). We formally define this *balanced influence maximization* problem and prove its NP-hardness. To solve it, we propose a linear programming relaxation coupled with randomized rounding, and design an efficient greedy heuristic algorithm augmented with a balancing correction mechanism to ensure both computational scalability and solution quality. Extensive experiments on real-world trajectory and digital billboard datasets demonstrate that our approach significantly outperforms multiple baselines: it achieves higher total influence while effectively maintaining exposure fairness across products—thus jointly optimizing overall efficacy and equitable allocation.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The billboard advertisement has emerged as an effective out-of-home advertisement technique where the objective is to choose a limited number of slots to play some advertisement content (e.g., animation, video, etc.) with the hope that the content will be visible to a large number of travelers, and this will be helpful to earn more revenue. In this paper, we study a variant of the influential slot selection problem where the advertiser wants to promote multiple products. Formally, we call this problem the extsc{Multi-Product Influence Maximization Problem for the Balanced Popularity} Problem. The input to our problem is a trajectory and a billboard database, as well as a budget for each product. The goal here is to choose a subset of slots for each product such that the aggregated influence of all the products gets maximized subject to the following two constraints: total selection cost for each product is less than or equal to the allocated budget for that product, and the difference between the influence for any two products is less than or equal to a given threshold. We show that the problem is NP-hard to solve optimally. We formulate this problem as a linear programming problem and use linear programming relaxation with randomized rounding. Further, we propose a greedy-based heuristic with balance correction to solve this problem. We conduct a number of experiments with real-world trajectory and billboard datasets, and the results are reported. From the reported results, we observe that the proposed solution approaches lead to more influence compared to many baseline methods.

Problem

• Maximizing multi-product advertisement influence under budget constraints
• Balancing product visibility across multiple promotional campaigns
• Selecting optimal billboard slots for multiple products efficiently

Innovation

• Linear programming relaxation with randomized rounding
• Greedy heuristic with balance correction
• Multi-product influence maximization under budget constraints