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图表卷积网络和自我注意力,用于顺序推
1Fuzhou University, Fuzhou, Fujian, China.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了GSASRec模型,通过使用图形卷积网络和对比学习来分散项目嵌入来增强顺序推系统,以提高预测性能.
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:
- 顺序推系统 (SRS) 对于个性化推至关重要,但与低于最佳的项目嵌入表示作斗争.
- 现有的方法经常受到物品嵌入矢量度的影响,限制了预测准确性.
研究的目的:
- 提出一种新型模型,GSASRec,可以改善SRS中的项目表示学习.
- 为了增强复杂的用户-项目关系的捕获,并分散项目嵌入.
主要方法:
- 使用图形卷积神经网络 (GCN) 来建模用户项目交互并学习节点嵌入.
- 基于用户项嵌入序列的结果预测采用自我注意序列模型.
- 整合实例智能对比学习 (ICL) 和原型对比学习 (PCL) 来完善表示学习.
主要成果:
- 拟议的GSASRec模型在四个不同的数据集中表现出卓越的性能.
- 废除研究证实了集成GCN,自我注意力和对比学习组件的有效性.
- 该方法成功地分散了项目嵌入,克服了先前技术的局限性.
结论:
- 通过有效地解决项目嵌入挑战,GSASRec为顺序推系统提供了重大进步.
- 图形网络和对比学习的结合为SRS中的表示学习提供了一个强大的框架.
- 这种方法可以证明更好的个性化建议.
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