基于图形神经网络和自我注意力增强的下一个应用程序预测
Junxin Chen1, Zhiqiong Liu2, Jing Liu2
1Cloud Network Operation Technology Research Institute, China Telecom Research Institute, Guangzhou, 510630, China. chenjx60@chinatelecom.cn.
Scientific reports
|July 2, 2025
概括
这项研究引入了下一个移动应用预测的新框架,通过捕捉不断变化的用户兴趣和时间动态来改进个性化建议. 该模型通过基于历史使用行为的更准确的应用程序建议来增强用户体验.
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:
- 预测下一个移动应用程序对于优化应用程序预加载和个性化建议至关重要,提高移动用户体验.
- 现有的模型在稀疏的交互,应用生态系统的增长,不断变化的用户偏好和时间动态方面扎.
研究的目的:
- 开发一个时间个性化的下一个应用程序预测框架,解决个性化特征提取和时间动态建模的局限性.
- 通过多视角图表表示学习和自我注意机制来增强用户和应用程序的嵌入.
主要方法:
- 使用多视角图表表示学习与自我注意机制来增强用户和应用程序的嵌入.
- 捕捉了长期和短期不断变化的用户兴趣和动态时间特征.
- 通过多视角特征聚合和上下文意识的注意力融合,整合全球互动.
主要成果:
- 拟议的框架有效地捕捉了不断变化的用户兴趣和动态的时间特征.
- 多视角的特征聚合和注意力融合整合了时间和个性化的特征.
- 在真实数据集上的实验结果表明,与基线相比,下一个应用程序的预测精确度更高.
结论:
- 时间个性化的下一个应用程序预测框架显著提高了预测准确性.
- 该模型捕捉动态时间特征和个性化兴趣的能力提高了其有效性.
- 这种方法为下一个应用程序的预测挑战提供了一个强大的解决方案.
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