通过跨领域的深度建议来增强企业知识检索:稀疏数据方法.
Ting Li1,2
1Shanghai Keyao Industrial Co. Ltd., Shanghai, 200023, China. Alfie.Clineadv@mail.com.
Scientific reports
|May 20, 2025
概括
本研究引入了一个跨领域推模型 (CDR-VAE),以克服企业知识管理中的稀疏数据挑战. 通过有效地跨领域转移知识,提高系统性能,CDR-VAE增强了建议.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 机器学习 机器学习
背景情况:
- 企业知识检索受到数据稀疏性和不高效的跨领域知识传输的阻碍.
- 传统的推方法在企业环境中难以应对这些挑战.
研究的目的:
- 为企业知识管理开发和评估一种新的跨领域推模型 (CDR-VAE).
- 通过深度学习解决数据稀疏性,改善跨领域的知识转移.
主要方法:
- 开发了一个带有域对齐的混合自编码器,称为CDR-VAE.
- 在企业数据集和电影和书籍基准上测试了该模型.
- 通过HR,回忆和NDCG等指标评估推绩效.
主要成果:
- 与现有模型相比,CDR-VAE实现了更高的性能 (HR=0.642,回忆=0.432,NDCG=0.715).
- 共同的潜在表示对于有效的跨领域学习至关重要.
- 该模型在稀疏的企业场景中显示出强度,用户更喜欢相关的内容类型.
结论:
- CDR-VAE有效地减轻了企业知识管理中的稀疏性和跨领域障碍.
- 该模型为数据稀缺环境中的基于深度学习的推系统提供了理论和实际见解.
- 这项研究推动了知识检索和推系统领域的发展.
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