Tag-specific Regret Minimization Problem in Outdoor Advertising

2026-03-06

AI Summary

This study addresses the label assignment problem in outdoor advertising, aiming to minimize total regret—which is non-monotonic and non-submodular—while satisfying advertisers’ influence requirements and budget constraints. To tackle this NP-hard and inapproximable problem, the authors propose a combinatorial optimization model termed TRMOA and develop a fairness-aware greedy round-robin strategy that integrates randomized greedy selection with local search. Experimental evaluations on real-world user trajectory and billboard datasets demonstrate that the proposed approach significantly reduces regret and enhances allocation efficiency, effectively balancing fairness and computational tractability.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recently, out-of-home advertising has become a popular marketing technique, due to its higher return on investment. E-commerce houses approach the influence provider to achieve effective advertising through their tags (advertising content), influence demand, and budgets. The influence provider's goal will be to make proper tag allocations, meet the required influence demand within the budget constraint, and minimize total regret. We formalize this as a combinatorial optimization problem and refer to it as \textsc{Tag-specific Regret Minimization in Outdoor Advertising (TRMOA)}. We show that TRMOA is NP-hard and inapproximable within a constant factor. The regret model we consider is non-monotone and non-submodular, and the simple greedy approach is ineffective. We introduce a fairness-aware greedy round-robin approach that reduces regret with balanced allocation across advertisers. To improve, we also introduce randomized greedy and local search algorithms. We have experimented with all the methodologies using real-world trajectory and billboard datasets to show the effectiveness and efficiency of the solution methodologies.

Problem

• outdoor advertising
• regret minimization
• tag allocation
• influence demand
• budget constraint

Innovation

• Regret Minimization
• Outdoor Advertising
• Non-submodular Optimization
• Fair Allocation
• Combinatorial Optimization