以知识加强的跨领域建议
概括
本研究引入了一种新的知识增强跨领域建议 (KR-CDR) 方法,以提高冷启动用户的准确性. 通过利用知识图 (KGs) 和强化学习 (RL),KR-CDR有效地将用户偏好跨域转移.
科学领域:
- 人工智能的人工智能
- 推系统是一个推系统.
- 数据科学数据科学数据科学
背景情况:
- 跨领域推旨在通过转移用户偏好来解决冷启动问题.
- 当域之间重叠的用户不足时,现有的方法很困难,这会影响准确性.
- 知识图 (KG) 提供辅助信息来弥合领域的差距.
研究的目的:
- 提出一种新的知识增强跨领域建议 (KR-CDR) 方法.
- 在低重叠场景中利用KG来加强偏好转移.
- 提高跨领域推器的准确性和性能.
主要方法:
- 从源域和目标域KG构建一个跨域知识图 (CDKG).
- 在CDKG上使用强化学习 (RL) 和元学习来发现元路径.
- 为冷启动用户生成元路径聚合的嵌入矢量.
主要成果:
- KR-CDR方法有效地利用了来自CDKG的元路径.
- 获得的用户嵌入允许准确的评级预测.
- 实验结果表明,在五个现实数据集上,与最先进的方法相比,性能优越.
结论:
- 提出的KR-CDR方法成功地解决了现有方法的局限性.
- 利用KG和RL可以提高跨领域推的准确性,特别是对于冷启动用户.
- 在稀疏数据设置中,KR-CDR提供了一个强大的解决方案来提高推者系统的性能.
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