图表神经网络引导的对比学习用于顺序推
Xing-Yao Yang1, Feng Xu1, Jiong Yu1
1School of Software, Xinjiang University, 666, Shengli Road, Urumqi 830049, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
图形神经网络引导的对比学习 (GC4SRec) 通过在增强数据视图中确保语义相似性来增强顺序推. 这种方法有效地减轻了数据稀疏性,并改善了推绩效指标.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 推系统是一个推系统.
背景情况:
- 序列推系统通常使用对比学习来解决通过增加用户序列来解决数据稀疏性.
- 一个关键的限制是增强数据视图中的语义相似性的潜在损失,阻碍了性能.
- 现有的方法缺乏可靠的机制,以确保语义连贯性在数据增强过程中顺序推.
研究的目的:
- 为序列推 (GC4SRec) 提出一个新的图形神经网络引导的对比学习框架.
- 增强对比学习中增强数据视图的语义一致性,以进行顺序推.
- 为了提高整体性能,并减轻数据稀疏性在连续推任务.
主要方法:
- 利用图形神经网络 (GNN) 来获得有意义的用户嵌入.
- 包含一个编码器来计算指导数据增强的项目重要性得分.
- 开发了一种对比的视图构建方法,利用项目重要性得分和各种增强技术.
主要成果:
- 在三个公共数据集上,GC4SRec表现出了显著的改进.
- 该模型实现了1.4%的命中率增加和1.7%的正常化折扣累积收益 (NDCG) 增加.
- 实验结果验证了建议的指导对比学习方法的有效性.
结论:
- 图形神经网络引导的对比学习 (GC4SRec) 有效地解决了顺序推中的语义相似性挑战.
- 拟议的方法通过改善关键指标,如命中率和NDCG,来提高推的性能.
- 在顺序推系统中,GC4SRec为缓解数据稀疏性问题提供了一个有希望的解决方案.
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