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一个自我监督的小组推模型与合规意识
Yue Kou1, Dong Li2, Derong Shen1
1School of Computer Science and Engineering, Northeastern University, Shenyang, 110167, Liaoning, China.
Scientific reports
|October 15, 2025
概括
我们介绍了ConfGR,这是一个新的小组推模型,它解释了合规心理学. 这种方法通过整合社会选择和使用多动机超图卷积网络 (HGCN) 的影响力学来提高推准确性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 社交计算社会计算
背景情况:
- 团体推系统旨在满足多个用户的偏好.
- 符合性心理学显著影响了小组建议的质量.
- 现有的方法在小组建议中不充分解决合规动态.
研究的目的:
- 提出一种新的自我监督模型,ConfGR,用于增强小组推.
- 整合社会选择和社会影响的合规性观点.
- 提高团体推系统的准确性和可解释性.
主要方法:
- 开发了一个符合意识小组建议 (ConfGR) 模型.
- 使用多动态超图卷积网络 (HGCN) 来捕获合规性.
- 综合社会动机,零售动机和集团动机的HGCN.
- 采用自主监督学习来优化群体嵌入.
主要成果:
- ConfGR有效地整合了个人和团体的合规动态.
- 该模型以动态方式捕捉集团内部和跨集团的合规性变化.
- 在三个真实世界数据集上的实验结果表明,相对于最先进的方法,性能优越.
结论:
- 拟议的ConfGR模型在小组建议方面取得了重大进展.
- 对合规心理学的考虑提高了推系统的性能.
- ConfGR为未来的研究提供了一个强大的框架,以个性化和团队意识的建议.
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