区域影响约束下广告牌广告的遗憾最小化
Minimizing Regret in Billboard Advertisement under Zonal Influence Constraint
AI总结：
针对广告牌广告中影响力供需不匹配产生的遗憾损失问题，本文将其建模为离散优化问题，提出四种贪心类求解方法，经真实数据实验验证，随机化预算有效贪心方法可在合理计算时间内最小化总遗憾。
摘要：
在典型的广告牌广告模式中，影响力服务商拥有一定数量的数字广告牌，众多广告商向服务商付费，要求其广告内容获得特定数量的曝光。若服务商提供的影响力达到或超过广告商需求，就能获得全额付款，否则仅能获得部分付款。对影响力服务商而言，提供的影响力无论高于还是低于广告商需求，都会造成损失，这一损失被形式化为“遗憾（Regret）”。因此，服务商的目标是在广告商之间分配广告牌时段，使总遗憾最小化。本文将该问题建模为离散优化问题，并提出四种求解方法。第一种方法以增量贪心方式从可用广告牌时段中进行选择，我们称之为预算有效贪心（Budget Effective Greedy）方法。第二种方法在第一种的基础上引入随机性，即对随机选取的部分广告牌时段样本计算边际收益。其余两种方法是对第二种方法的进一步改进。本文分析了所有算法的时间复杂度与空间复杂度，基于真实轨迹数据集与广告牌数据集实现了这些算法，并开展了多组实验。实验结果表明，随机化预算有效贪心方法在最小化遗憾的同时，所需计算时间处于合理范围内。
英文摘要：
In a typical billboard advertisement technique, a number of digital billboards are owned by an influence provider, and many advertisers approach the influence provider for a specific number of views of their advertisement content on a payment basis. If the influence provider provides the demanded or more influence, then he will receive the full payment or else a partial payment. In the context of an influence provider, if he provides more or less than an advertiser's demanded influence, it is a loss for him. This is formalized as 'Regret', and naturally, in the context of the influence provider, the goal will be to allocate the billboard slots among the advertisers such that the total regret is minimized. In this paper, we study this problem as a discrete optimization problem and propose four solution approaches. The first one selects the billboard slots from the available ones in an incremental greedy manner, and we call this method the Budget Effective Greedy approach. In the second one, we introduce randomness with the first one, where we perform the marginal gain computation for a sample of randomly chosen billboard slots. The remaining two approaches are further improvements over the second one. We analyze all the algorithms to understand their time and space complexity. We implement them with real-life trajectory and billboard datasets and conduct a number of experiments. It has been observed that the randomized budget effective greedy approach takes reasonable computational time while minimizing the regret.
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。
