一种有效的广告牌广告标签分配方法
An Effective Tag Assignment Approach for Billboard Advertisement
AI总结：
本文研究广告牌广告中的标签分配问题，将其建模为一对多二分匹配，并提出一种迭代求解方法，在真实数据集上验证了其有效性与高效性。
摘要：
广告牌广告因其显著的投资回报率而日益受到欢迎。为了使这种广告方式更加有效，产品的相关信息需要触达相关人群。如果能够将相关标签集合映射到正确的广告牌时段，就可以实现这一目标。形式上，我们将该问题称为广告牌广告中的标签分配问题。给定轨迹、广告牌数据库以及一组选定的广告牌时段和标签，该问题要求输出选定标签到选定时段的映射，以使影响力最大化。我们将此问题建模为传统二分匹配的一种变体，称为一对多二分匹配（OMBM）。与传统二分匹配不同，在传统二分匹配中一个标签只能分配给一个时段；而在OMBM中，一个标签可以分配给多个时段，反之则不允许。我们提出了一种迭代求解方法，逐步将标签分配到时段。所提出的方法已通过一个示例进行了说明，并对所提出的求解方法进行了复杂度分析。在真实世界轨迹和广告牌数据集上的实验结果证明了我们所提出解决方案的有效性和高效性。
英文摘要：
Billboard Advertisement has gained popularity due to its significant outrage in return on investment. To make this advertisement approach more effective, the relevant information about the product needs to be reached to the relevant set of people. This can be achieved if the relevant set of tags can be mapped to the correct slots. Formally, we call this problem the Tag Assignment Problem in Billboard Advertisement. Given trajectory, billboard database, and a set of selected billboard slots and tags, this problem asks to output a mapping of selected tags to the selected slots so that the influence is maximized. We model this as a variant of traditional bipartite matching called One-To-Many Bipartite Matching (OMBM). Unlike traditional bipartite matching, a tag can be assigned to only one slot; in the OMBM, a tag can be assigned to multiple slots while the vice versa can not happen. We propose an iterative solution approach that incrementally allocates the tags to the slots. The proposed methodology has been explained with an illustrated example. A complexity analysis of the proposed solution approach has also been conducted. The experimental results on real-world trajectory and billboard datasets prove our claim on the effectiveness and efficiency of the proposed solution.
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。
