社区结构增强和消除多行为建议的方法
Wei Cai1, ZhiHong Zheng1, Xuan Zhang2
1School of Software, Yunnan University, Yunnan 650091, China.
概括
本研究介绍了邻居结构增强和排斥方法 (NSED),以改进多行为推系统. 通过增强图形结构和消除跨行为信息以提高准确性,NSED有效地模拟用户偏好.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的推系统往往过于简化了用户交互,假设只有一个行为类型.
- 现实世界的用户参与涉及复杂的,多方面的行为,如浏览,点击,添加到购物车和购买.
- 现有的多行为推方法面临着数据不平衡,信息稀疏和辅助行为噪音的挑战.
研究的目的:
- 解决当前多行为推系统的局限性,特别是不平衡的数据和噪音.
- 提出一种新的方法,即邻里结构增强和取消 (NSED),用于更准确的用户偏好建模.
- 为了增强邻近节点的表示,并减轻推系统中的长尾问题.
主要方法:
- 实现了一个社区增强的图形卷积网络 (GCN),以加强节点表示.
- 采用结构增强模块来解决长尾问题并改善邻居信息.
- 通过级联结构利用交叉行为建模来发现不同用户行为之间的依赖关系.
- 集成了一个无声化模块与对比学习,以减轻负迁移和完善辅助行为信息.
主要成果:
- 通过加强社区结构和消除跨行为数据,NSED显著改善了用户偏好建模.
- 该方法通过级联结构有效地捕捉了各种用户行为之间的依赖关系.
- 在目标行为图中学习的用户偏好显示出高准确性,而辅助行为图被有效地拒绝.
- 在三个公共数据集上,NSED在SOTA基线上实现了平均10.4%和10.67%的性能改善.
结论:
- 通过增强图形结构和减少噪音,NSED为多行为推系统提供了强大的解决方案.
- 提出的方法有效地模拟了复杂的用户偏好,从而带来了显著的性能提升.
- 纳德成功地解决了包括数据不平衡,信息稀疏和负面迁移现象在内的关键挑战.
- 该方法与现有的最先进的方法相比,表现出优越的性能,在多个基准数据集上得到验证.
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