通过频繁的项目集挖矿融合算法,提供高效准确的个性化产品推
1Jiaozuo Normal College, Jiaozuo, 454000, China.
Heliyon
|February 5, 2024
概括
本研究引入了一种新的融合建议算法,使用频繁的项目集挖掘来过无效数据并改进电子商务建议. 该算法通过分析用户兴趣和产品关系来提高准确性和效率.
科学领域:
- 电子商务是电子商务.
- 数据挖掘 数据挖掘
- 推系统是一个推系统.
背景情况:
- 个性化产品推系统经常与稀疏的数据和冷启动问题作斗争.
- 在大型电子商务数据集中过无效信息是一个重大,未被充分探索的挑战.
- 基于频繁的项目集挖掘的现有融合推算法面临诸如冗余规则和低准确度等问题.
研究的目的:
- 提出一种新的融合推算法,以应对在电子商务中过无效信息的挑战.
- 提高个性化产品推的准确性和效率.
- 适应用户的动态偏好,并实时捕捉不断变化的兴趣.
主要方法:
- 开发了一个基于频繁项目集挖掘的融合推算法.
- 实施数据集压缩和识别频繁的商品集.
- 包含了用户商品兴趣排名的计算和类似产品推规则的定义.
主要成果:
- 拟议的算法有效地过了无效的商品数据,提高了推质量.
- 通过减少候选人的频繁项目集,证明了更好的时间效率.
- 通过用户-商品兴趣分析和类似产品识别,实现了更准确的建议.
结论:
- 该算法有效过电子商务数据,从而提供更准确,更高效的个性化建议.
- 它适应动态用户偏好,增强电子商务平台的用户体验.
- 相对分析显示,与其他数据挖掘算法相比,数据集和操作时间减少了.
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