通过多兴趣的超标代表网络增强基于会话的建议
概括
这项研究引入了一个基于会话的推 (SBR) 的新网络,它使用过度几何来更好地理解用户交互. 多兴趣过度表达网络 (MIHRN) 显著提高了下一个点击项目的预测准确性.
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:
- 基于会话的推 (SBR) 预测用户在单个会话中的行为,与依赖用户历史的传统方法不同.
- 当前的SBR模型经常在欧几里德空间中使用图形网络,这可能无法捕捉复杂的会话结构和用户兴趣的多样性.
研究的目的:
- 为增强基于会话的推提出一个新的多利益过度表达网络 (MIHRN).
- 在简短,复杂的用户会话中解决建模层次结构和各种用户兴趣的局限性.
主要方法:
- 采用过度几何学来建模复杂的高阶空间结构和项目之间的顺序关系.
- 使用超级图形神经网络来捕捉会话中的高阶关系和局部集群.
- 包含多方面利益代表模块,以建模用户利益的多样性.
主要成果:
- 拟议的MIHRN在三个真实世界数据集中实现了显著的性能改进.
- 在P@10指标下表现出显著的收益,改善率为23.81%,14.81%和36.84%.
结论:
- MIHRN有效地使用超标几何学模拟复杂的会话动态和各种用户兴趣.
- 该方法为推进基于会话的推系统的准确性和能力提供了一个有希望的方向.
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