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.

翻译：近年来，户外广告因其较高的投资回报率而成为一种流行的营销技术。电子商务平台通过其标签（广告内容）、影响力需求和预算，与影响力提供方合作以实现有效广告。影响力提供方的目标将是进行适当的标签分配，在预算约束下满足所需的影响力需求，并最小化总后悔值。我们将此形式化为一个组合优化问题，并称之为 \textsc{Tag-specific Regret Minimization in Outdoor Advertising (TRMOA)}。我们证明 TRMOA 是 NP 难问题，且无法在常数因子内近似求解。我们考虑的后悔模型是非单调且非子模的，简单的贪心方法效果不佳。我们引入了一种公平感知的贪心轮询方法，通过平衡广告商之间的分配来减少后悔。为了进一步改进，我们还引入了随机化贪心和局部搜索算法。我们使用真实世界的轨迹和广告牌数据集对所有方法进行了实验，以展示解决方案的有效性和效率。