Tag-specific Regret Minimization Problem in Outdoor Advertising

TL;DR

This paper formulates a complex outdoor advertising optimization problem focused on tag-specific regret minimization, demonstrating its computational difficulty and proposing several algorithms validated on real data.

Contribution

It introduces the TRMOA problem, proves its NP-hardness, and develops new algorithms including a fairness-aware greedy, randomized greedy, and local search methods.

Findings

The problem is NP-hard and inapproximable within a constant factor.

The proposed algorithms effectively reduce regret in real-world datasets.

Balanced allocation improves fairness and overall regret minimization.

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.