户外广告中的特定标签 regret 最小化问题
Tag-specific Regret Minimization Problem in Outdoor Advertising
AI总结：
本文提出了一种基于公平性的贪心轮转方法，用于解决户外广告中特定标签的 regret 最小化问题，并通过实验验证了其有效性。
摘要：
近年来，户外广告已成为一种受欢迎的营销技术，由于其更高的投资回报率。电商平台通过其标签（广告内容）、影响力需求和预算向影响力提供者寻求有效的广告。影响力提供者的目的是进行适当的标签分配，满足预算约束下的所需影响力需求，并最小化总 regret。我们将此问题形式化为一个组合优化问题，并称之为“户外广告中的特定标签 regret 最小化（TRMOA）”。我们证明 TRMOA 是 NP 难的，并且在常数因子内不可近似。我们考虑的 regret 模型是非单调和非子模的，简单的贪心方法是无效的。我们引入了一种公平意识的贪心轮转方法，以通过在广告商之间平衡分配来减少 regret。为了进一步改进，我们还引入了随机贪心和局部搜索算法。我们使用真实世界轨迹和广告牌数据集对所有方法进行了实验，以展示解决方案方法的有效性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
