预测产品受欢迎程度的基于多代理系统的注意力机制:在社交网络上处理正负传播
IEEE transactions on neural networks and learning systems
|November 16, 2023
概括
本研究引入了一种新的模型,用于使用正负扩散 (PND) 在社交网络 (SN) 上预测产品的受欢迎程度. 开发的多代理系统注意力机制 (MASAM) 准确地捕获用户特征,以改善扩散预测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 社交网络分析 社交网络分析
背景情况:
- 预测产品的受欢迎程度对于营销和商业战略至关重要.
- 社交网络显著影响产品传播动态.
- 现有的模型往往难以捕捉复杂的用户交互和扩散模式.
研究的目的:
- 提出一种新的模型来预测产品在社交网络上的受欢迎程度,考虑正负扩散 (PND).
- 开发一种高效的特征提取方法,用于扩散预测中的用户代表性.
- 建立一个模拟和预测产品扩散的多代理系统 (MAS) 模型.
主要方法:
- 开发了一个正负扩散 (PND) 模型来模拟产品的传播.
- 一个基于多代理系统的注意力机制 (MASAM) 为最佳的用户特征向量提取而设计.
- 使用分布式学习算法来训练MASAM的共享重量矩阵.
- 使用MASAM特征表示,建立了产品扩散的MAS模型.
- 建议使用代理互动规则来加速模拟.
主要成果:
- 拟议的PND模型和MASAM有效地模拟了产品在社交网络上的传播.
- MASAM显著提高了用于预测的用户特征提取的精度.
- 马斯模型在产品受欢迎程度预测方面表现出高的有效性和效率.
- 实验结果显示,与基线方法相比,性能优越.
- 一个案例研究验证了算法的适用性和可扩展性.
结论:
- 由MASAM驱动的开发的PND和MAS模型提供了一个强大的解决方案,用于在社交网络上预测产品的受欢迎程度.
- 该方法通过有效建模用户行为和扩散动态,提供准确和高效的预测.
- 这些发现对营销策略和理解信息传播有实际意义.
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