论文标题
部分可观测时空混沌系统的无模型预测
Influential Billboard Slot Selection using Pruned Submodularity Graph
论文作者
论文摘要
广告牌广告已成为一种有效的户外广告技术，并被许多商业房屋采用。在这种情况下，广告牌由某些公司拥有，并以付款方式将其提供给商业房屋{ - }明智的方面。现在，鉴于广告牌的数据库以及其插槽信息，应选择$ k $插槽以最大程度地发挥影响力。正式地，我们将此问题称为\ textsc {有影响力的广告牌插槽选择}问题。在本文中，我们将此问题作为组合优化问题提出。在“影响的触发模型”下，影响函数是非负，单调和子模型的。但是，由于对于问题实例的大小而言，由于亚辅导功能最大化的增量贪婪方法不能很好地扩展，因此需要为此问题开发有效的解决方案方法。
Billboard Advertisement has emerged as an effective out-of-home advertisement technique and adopted by many commercial houses. In this case, the billboards are owned by some companies and they are provided to the commercial houses slot\mbox{-}wise on a payment basis. Now, given the database of billboards along with their slot information which $k$ slots should be chosen to maximize the influence. Formally, we call this problem as the \textsc{Influential Billboard Slot Selection} Problem. In this paper, we pose this problem as a combinatorial optimization problem. Under the `triggering model of influence', the influence function is non-negative, monotone, and submodular. However, as the incremental greedy approach for submodular function maximization does not scale well along with the size of the problem instances, there is a need to develop efficient solution methodologies for this problem.
