一种使用对比学习和瓦斯斯坦自我注意力机制的顺序推方法
Shengbin Liang1, Jinfeng Ma1, Qiuchen Zhao2
1School of Software, Henan University, Kaifeng, Henan, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种用于顺序推系统的新型随机自我注意方法,提高了准确性和处理不可预测的用户行为. 该方法增强了协作转移学习,并优于现有模型,特别是对于新项目.
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:
- 基于变压器的序列编码器对于序列推是有效的.
- 现有的点点产品自我注意方法与不可预测的用户行为扎,并捕捉协作可转移性.
- 贝叶斯个性化排名 (BPR) 损失缺乏约束,导致低于最佳的优化.
研究的目的:
- 提出一种使用随机自我注意的新方法,以解决顺序推的局限性.
- 通过纳入不确定性来增强特征表示和协作转移学习.
- 提高推的性能,特别是对于冷启动产品.
主要方法:
- 引入了使用圆高斯分布与平均值和共变量向量引入的不确定性.
- 雇佣了瓦瑟斯坦的自我注意力来计算位置关系,并纳入不确定性.
- 使用cloze和dropout面具机制生成高质量的阳性样本,用于多对对比学习.
- 实施了动态损失重权策略,以平衡封闭和对比损失.
主要成果:
- 与最先进的方法相比,拟议的模型表现出优越的性能.
- 在多个数据集 (美容,玩具,ML-1M,ML-100M) 中,观察到命中率 (HR) 和正常化折扣累积收益 (NDCG) 的显著改善.
- 该模型在处理冷启动项目方面表现出特别高的效率.
结论:
- 新的随机自我注意方法有效地解决了用户行为在顺序推中的不可预测性.
- 瓦瑟斯坦自我注意和高斯分布的整合增强了协作转移学习和模型稳定性.
- 提出的方法为改善推准确性和适应性提供了一个有希望的解决方案,特别是在用户数据有限的场景中.
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