主导偏好脱和引导扰乱偏好注入用于跨域序列推.
Xiuze Li1, Zhenhua Huang2, Changdong Wang3
1School of Computer Science, South China Normal University, Guangzhou, 510631, Guangdong, China; School of Artificial Intelligence, South China Normal University, Foshan, 528225, Guangdong, China.
概括
这项研究引入了一种新的跨领域推模型 (DP-CSR),通过分离主导和扰乱用户偏好,更好地预测用户偏好. 通过有效利用域内和跨域用户行为数据,DP-CSR提高了推准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 跨域序列推模型跨不同域的用户交互.
- 现有的方法往往无法解释主导的域内偏好和扰乱偏好的影响.
研究的目的:
- 为跨域序列推 (DP-CSR) 提出一种新型模型,主导偏好脱和指导扰乱偏好注入.
- 通过保留主导的域内偏好和利用扰乱的跨域信息来解决当前跨域推系统的局限性.
主要方法:
- DP-CSR采用多通道超图学习和注意力机制,以捕捉多种域内主导偏好.
- 它使用序列编码器和一个门机制来动态注入跨域扰乱偏好信息.
- 对比式学习用于偏好脱和对准域内和跨域扰乱偏好.
主要成果:
- 与最先进的方法相比,DP-CSR显示出显著和一致的改进.
- 在六个现实数据集上的实验验验证了该模型在跨域序列推中的有效性.
结论:
- 拟议的DP-CSR模型通过整合主导和扰乱偏好,有效地提高了预测适应性.
- 该方法成功地将偏好脱和调整,从而在跨领域的推任务中获得更高的性能.
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