基于改进的K-means商品信息管理模型的电子商务推系统
1School of mathematics, South China University of Technology, Guangzhou, 510641, Guangdong, China.
本研究介绍了电子商务推系统的增强K-means集群算法,显著提高了准确性和效率. 这种精细的算法达到91.1%的准确性,超过了传统的方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 电子商务 技术 技术 电子商务
背景情况:
- 互联网和智能手机技术的快速发展推动了电子商务的增长.
- 目前的电子商务推系统落后,影响了效率和准确性.
- 需要改进推算法,以满足不断变化的电子商务需求.
研究的目的:
- 为电子商务推系统开发一个增强的K-means集群算法.
- 提高电子商务中商品信息管理的效率和准确性.
- 为快速增长的电子商务行业提供更有效的建议解决方案.
主要方法:
- 整合K-means集群算法与遗传算法.
- 遗传算法编码的实施,最初的人口设置和健身功能定义.
- 使用增强的K-means集群方法管理商品信息.
主要成果:
- 改进的K-means算法实现了91.1%的推准确度.
- 这种准确性超过了传统的K-平均 (87.9%) 和模糊的C-平均 (84.8%) 算法.
- 改进的K-means算法显示了比传统的K-means更快的44%的收率.
结论:
- 精细的K-means集群算法显著提高了电子商务中的推熟练度和精度.
- 这种增强的算法为现有的推技术提供了更好的替代方案.
- 该研究通过改进的推技术,有助于推进电子商务行业.
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