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SIIR:对称信息交互模拟新闻推建议
IEEE transactions on neural networks and learning systems
|August 14, 2023
概括
这项研究引入了对称信息交互建模 (SIIR) 以改进新闻推. SIIR增强了用户兴趣和候选新闻的匹配,提高了点击率预测的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 新闻推系统依赖于用户兴趣和候选新闻之间的准确匹配.
- 现有的模型经常受到用户和候选新闻表示之间的不对称信息密度的影响,这会影响预测准确性.
- 这种不对称性导致新闻推中的点击率预测不足.
研究的目的:
- 为新闻推提出一种新的对称信息交互建模 (SIIR).
- 解决用户和候选新闻表示中的信息不对称问题.
- 为了提高新闻推系统中点击率预测的准确性.
主要方法:
- 设计了一个用于用户 (LIAU) 建模的轻度交互式注意网络,以提取相关的用户兴趣并减少噪音.
- 开发了一种异质图形神经网络 (HGNN),以利用新闻之间的关系来增强候选新闻的表现.
- 实施SIIR,以创建一个对称的信息交互框架,用于新闻推.
主要成果:
- SIIR有效地提取与候选新闻相关的用户兴趣,同时尽量减少噪音干扰.
- 通过利用潜在的新闻关系,HGNN提高了候选新闻的代表性,减轻了冷启动问题.
- 对MIND和Adressa数据集的实验表明,SIIR显著超过了最先进的单一模型方法.
结论:
- SIIR提供了对称的信息交互方法,解决了新闻推中的不对称问题.
- 拟议的LIAU和HGNN组件分别有效地模拟用户利益和候选新闻代表.
- SIIR在新闻推任务中表现出卓越的表现,为未来的研究提供了有希望的方向.
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