基于功能重新缩放的联合交叉视图电子商务建议
Ruiheng Li1,2,3, Yuhang Shu1,4, Yue Cao1,4
1Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, 430205, China.
Scientific reports
|December 2, 2024
概括
本研究介绍了Fed-FR-MVD,这是一个新的联合学习框架,增强电子商务建议. 它通过整合多视图学习和特征表示来提高准确性和效率,解决隐私问题.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 集中推系统引发了数据隐私问题.
- 联合学习在客户端设备上训练模型,保持原始数据隐私.
- 联合学习在电子商务中与特征提取效率和噪音作斗争.
研究的目的:
- 介绍Fed-FR-MVD,一个多视图联合学习框架.
- 提高功能提取效率和电子商务推准确度.
- 在联合推系统中解决数据异质性和噪声敏感性.
主要方法:
- 开发了一个新的多视图联合学习框架 (Fed-FR-MVD).
- 在多视图结构中集成了一个特征表示 (FR) 机制.
- 整合了项目和用户视角,以提供强大的特征表示.
- 使用动态重新缩放来优化功能利用和减轻噪音.
主要成果:
- 与现有方法相比,建议准确度提高了12%至18%.
- 在5%-15%的噪音水平上,证明了持续的性能.
- 在处理数据异质性方面展示了更好的弹性和效率.
结论:
- 美联储-FR-MVD为联邦推系统提供了一个更有弹性和更有效的框架.
- 该框架有效地解决了电子商务环境中的隐私问题.
- 它增强了特征表示和稳定性,这对于数据多样化的设置至关重要.
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