联邦PCL-CDR:一个基于联邦原型的对比学习框架,用于维护隐私的跨领域建议
1School of Electrical and Data Engineering, University of Technology Sydney, 15, Broadway, Ultimo, Sydney, 2000, NSW, Australia.
概括
本研究介绍了FedPCL-CDR,这是一个保护隐私的跨域推 (CDR) 框架. 它通过使用联合学习和差异原型来提高推准确性,即使没有重叠的用户数据.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 跨领域建议 (CDR) 利用数据丰富的领域来改进稀疏领域的建议.
- 现有的CDR方法经常忽视用户隐私,因为它们需要公众交互数据.
- 性能下降,几乎没有重叠的用户,限制了知识传输.
研究的目的:
- 为保护隐私的CDR (FedPCL-CDR) 提出一个新的基于联邦原型的对比学习 (CL) 框架.
- 为了解决隐私问题,并通过不重叠的用户数据提高CDR性能.
- 通过联合学习,增强稀疏领域的知识转移.
主要方法:
- 联合学习框架与本地客户端学习和全球服务器聚合.
- 本地差异隐私 (LDP) 来从用户数据中学习差异原型.
- 使用本地和全球差异原型进行知识转移的对比学习.
主要成果:
- 在四个CDR任务中,FedPCL-CDR显著超过了最先进的 (SOTA) 基线.
- 在HR@10中实现了5.76%的平均改善,在NDCG@10中达到7.36%,在MRR@10中达到13.53%.
- 在保护隐私的同时利用非重叠的用户信息的证明有效性.
结论:
- FedPCL-CDR为跨域推提供了一种保护隐私和有效的解决方案.
- 该框架成功地处理了稀疏的重叠用户条件.
- 拟议的方法在推系统中推进了保护隐私的机器学习领域.
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