一种基于区块链的评估方法,使用人工智能技术分析客户满意度
Kousik Barik1, Sanjay Misra2,3, Ajoy Kumar Ray4
1Department of Computer Science, University of Alcala, Spain.
Heliyon
|June 9, 2023
概括
这项研究引入了一种基于区块链的新框架,用于预测在线消费者满意度. 先进的多维天真贝叶斯-K近邻和多目标物流粒子群优化算法模型显著提高了预测的准确性和效率.
科学领域:
- 计算机科学 计算机科学
- 信息系统信息系统信息系统
- 电子商务 技术 技术 电子商务
背景情况:
- 由于技术进步和不断变化的消费者需求,在线购物正在迅速发展.
- 预测客户满意度对于电子商务组织来说至关重要,以提高服务质量和决策.
- 信任和隐私平台是现代在线零售环境的组成部分.
研究的目的:
- 开发一个强大的框架来预测在线消费者满意度.
- 加强电子商务企业关于服务和质量的决策过程.
- 将区块链技术集成为可信的预测模型.
主要方法:
- 提出了一个基于区块链的框架,集成多维天真贝叶斯-K近邻 (MDNB-KNN) 算法.
- 使用多目标物流粒子群优化算法 (MOL-PSOA) 来进行优化.
- 使用回归模型量化生产因素对客户满意度的影响.
主要成果:
- 拟议的模型实现了高水平的测量:98%的客户满意度,95%的准确性,95%的精度和95%的回忆.
- 该模型显示,与现有方法相比,所需的时间大大减少,效率达到60%.
- 该框架提供了比当前研究更优越的绩效指标.
结论:
- 开发的基于区块链的框架有效地预测在线消费者满意度,准确度和效率高.
- 值得信赖的平台对于了解电子商务中的客户购买决策至关重要.
- 该研究为电子商务客户满意度测量的概念和实践进步做出了贡献.
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