相反的基于学习的个性化标签推
Aoran Zhang1, Yonghong Yu2, Shenglong Li2
1School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212000, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究引入了一种新的对比学习算法 (CLPTR),通过解决数据稀疏性来改进个性化标签推. CLPTR 增强了用户和项目表示,在标签推系统中实现了最先进的性能.
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:
- 个性化标签推系统旨在根据用户的喜好为用户提供相关的标签.
- 传统的方法与数据稀疏性作斗争,阻碍了准确的用户,项目和标签嵌入学习.
- 这种限制影响了个性化标签推模型的有效性.
研究的目的:
- 提出一种新的基于对比学习的个性化标签推算法 (CLPTR).
- 为了克服传统标签推系统中的数据稀疏性问题.
- 为了提高学习用户,项目和标签嵌入的准确性.
主要方法:
- 开发了CLPTR,这是一个用于个性化标签推的对比学习算法.
- 通过将噪音注入隐性特征,生成增强的用户标签和项目标签交互图.
- 集成的对比学习与图形神经网络 (GNN) 模型用于自我监督的信号提取.
主要成果:
- CLPTR有效地保留了交互图的语义和结构信息.
- 与传统模型相比,拟议的方法表明了最先进的性能.
- 在真实世界数据集上的广泛实验验证了CLPTR的有效性.
结论:
- 与传统的个性化标签推方法相比,CLPTR提供了显著的改进.
- 在图形增强中,噪声注入策略对于保护数据完整性至关重要.
- 这项研究通过有效的对比学习集成,推进了个性化推系统领域的发展.
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