多视图知识表示学习为个性化的新闻推建议
Chao Chang1, Feiyi Tang2, Peng Yang1,3
1School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, 511483, China.
Scientific reports
|January 8, 2025
概括
一个新的多视图知识表示学习 (MKRL) 框架通过将候选新闻整合到用户兴趣建模中来改进个性化的新闻建议,提高动态用户偏好的准确性和相关性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息检索 信息检索
背景情况:
- 个性化的新闻推面临着挑战,因为用户的兴趣多样化和动态化.
- 现有的模型很难将候选新闻整合到用户兴趣建模中,从而限制了准确性.
研究的目的:
- 提出多视图知识表示学习 (MKRL) 框架,以加强个性化的新闻推.
- 通过共同考虑用户历史和候选新闻特征来改进用户兴趣建模.
主要方法:
- 开发了具有多视图新闻编码器和候选人意识注意力机制的MKRL框架.
- 集成的卷积神经网络和多头注意力用于上下文信息捕获.
- 根据相关性,动态权衡用户交互和候选新闻.
主要成果:
- 在MKRL框架中,比最先进的基线表现出更高的性能.
- 在实验中实现了提升推准确性和相关性.
- 在三个真实世界数据集上验证了有效性.
结论:
- 在MKRL框架有效地捕捉动态用户的兴趣个性化新闻.
- 将候选人新闻直接纳入建模可以显著提高推质量.
- 多视图方法可以提供更丰富,更个性化的新闻建议.
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