基于多层次对比学习的增强知识图推算法
Zhang Rong1, Liu Yuan2, Li Yang3
1School of Internet of Things Engineering, Jiangsu Vocational College of Information Technology, Wuxi, 214153, China. 95291188@qq.com.
Scientific reports
|October 4, 2024
概括
本研究引入了基于知识图的推系统的新框架,使用多层次的对比学习来克服数据稀疏性并提高个性化准确性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 知识图 (KG) 改进了推系统,但由于长尾分布,数据稀疏.
- 现有的方法很难有效地利用KG信息进行个性化建议.
研究的目的:
- 提出一个以知识为基础的推框架,解决KG中的数据稀疏性.
- 通过多层次的对比学习来提高推准确性和个性化.
主要方法:
- 一个协作知识图 (CKG) 使用随机边缘掉落来增强多层次特征表示 (用户-用户,项目-项目,用户-项目).
- 图表注意力网络 (GAT) 具有动态注意力机制模型的KG.
- 多层次的对比学习,包括非线性转换和动量对比 (Moco),被用作辅助的自我监督任务.
主要成果:
- 该框架有效地从KG中提取高质量的特征信息.
- 在MovieLens和亚马逊书籍数据集上的实验结果显示,推性能显著改善.
- 拟议的方法在各种评估指标中表现优于基线模型.
结论:
- 基于知识的推框架成功地缓解了KG中的数据稀疏性问题.
- 多层次的对比学习提高了基于KG的推系统的有效性.
- 该方法为稀疏数据环境中的个性化建议提供了强大的解决方案.
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