以知识为导向的语义一致的对比学习,用于顺序推
Chenglong Shi1, Surong Yan1, Shuai Zhang1
1Zhejiang University of Finance and Economics, Hangzhou, 310018, China.
概括
以知识为导向的对比学习通过使用项目知识图表来创建语义上一致的数据视图来增强顺序推. 这种方法克服了随机增强的局限性,提高了推的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 在处理数据稀疏性的顺序建议中,对比式学习占主导地位.
- 现有的方法由于随机增强的不一致语义而遭受性能退化.
- 信息稀缺性限制了当前增强策略的有效性.
研究的目的:
- 提出一个新的知识导向语义一致的对比学习 (KGSCL) 模型,用于顺序推.
- 利用项目知识图 (IKG) 来创建语义上一致的数据增强.
- 提高推的性能,稳定性和模型的融合.
主要方法:
- 引入了基于知识的增强操作 (KG-substitute,KG-insert) 使用IKG邻居.
- 开发了一种基于共发生的抽样策略,用于选择相关邻居.
- 实施了视图-目标对比学习 (CL) 方法,以建模视图和目标项目之间的相关性.
主要成果:
- 在六个数据集中,KGSCL表现出卓越的推性能.
- 与14个最先进的竞争对手相比,该车型显示出更强大的稳定性.
- KGSCL实现了更好的模型融合.
结论:
- 在对比学习中以知识为导向的语义一致性显著改善了顺序推.
- 利用项目知识图为解决增强挑战提供了一个有前途的方向.
- KGSCL为顺序推系统提供了有效和强大的解决方案.
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