在社交物联网中的在线广告的改进影响力最大化方法
Reza Molaei1, Kheirollah Rahsepar Fard1, Asgarali Bouyer2
1Department of Computer Engineering and Information Technology, University of Qom, Qom, Iran.
Big data
|August 1, 2023
概括
本研究介绍了社交物联网 (IMSoT) 算法的广告影响力最大化算法. IMSoT通过识别关键节点来增强广告覆盖范围,同时最大限度地减少重复广告和成本.
科学领域:
- 计算机科学 计算机科学
- 网络科学 网络科学
- 人工智能的人工智能
背景情况:
- 社交物联网 (SIoT) 整合了物联网和社交网络概念,在智能运输,医疗保健和病毒式营销中找到应用.
- 在SIoT广告中,一个关键的挑战是确定最佳的扩散节点,以最大限度地提高广告的覆盖范围.
- 现有的方法努力平衡影响力最大化与成本效益和广大受众参与.
研究的目的:
- 提出一种高效的启发式算法,IMSOT,用于SIoT广告中的影响力最大化.
- 通过识别关键节点来提高广告的有效性,同时最大限度地减少重复广告和相关成本.
- 考虑弱节点在SIoT网络中最大限度地扩大受众覆盖范围的影响.
主要方法:
- IMSoT算法采用两步方法:候选对象选择和最终种子集识别.
- 候选对象是根据程度,当地重要性和邻居分析 (弱和敏感) 进行选择的.
- 最终的种子组通过计算基于重叠的候选对象的影响来确定,以确保最大范围和最小冗余.
主要成果:
- IMSoT显著优于最先进的算法,在考虑弱对象方面表现出38%至193%的改进.
- 该算法有效地将重复广告成本降低了26%-77%.
- 与现有的算法相比,IMSOT的运行时间更短.
结论:
- IMSoT算法为SIoT广告的影响力最大化提供了高效和有效的解决方案.
- 通过专注于弱节点并防止广告重复,IMSoT优化了覆盖范围并降低了成本.
- IMSoT为社会物联网生态系统中的目标广告策略提供了宝贵的进步.
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