Creating and maximizing influence among the customers is one of the central goals of an advertiser, and hence, remains an active area of research in recent times. In this advertisement technique, the advertisers approach an influence provider for a specific number of views of their content on a payment basis. Now, if the influence provider can provide the required number of views or more, he will receive the full, else a partial payment. In the context of an influence provider, it is a loss for him if he offers more or less views. This is formalized as 'Regret', and naturally, in the context of the influence provider, the goal will be to minimize this quantity. In this paper, we solve this problem in the context of billboard advertisement and pose it as a discrete optimization problem. We propose four efficient solution approaches for this problem and analyze them to understand their time and space complexity. We implement all the solution methodologies with real-life datasets and compare the obtained results with the existing solution approaches from the literature. We observe that the proposed solutions lead to less regret while taking less computational time.

翻译：在客户中创造并最大化影响力是广告商的核心目标之一，因此近年来这一领域一直是活跃的研究方向。在这种广告技术中，广告商基于付费模式向影响力提供商申请特定次数的内容展示。若影响力提供商能够提供所需展示次数或更多，则可获得全额付款；否则只能获得部分付款。对影响力提供商而言，提供过多或过少展示次数均会造成损失。这一现象被形式化为"遗憾"（Regret），而影响力提供商的目标自然是最小化这一指标。本文针对广告牌广告场景解决该问题，并将其建模为离散优化问题。我们提出了四种高效的求解方法，并通过分析明确其时间与空间复杂度。所有求解方法均基于真实数据集实现，并将所得结果与文献中现有方案进行对比。实验表明，所提方案在显著降低计算时间的同时实现了更少的遗憾值。