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CPJN:新闻推与内容和人气联合网络的新闻推
Zixuan Chen1, Songqiao Han2, Hailiang Huang3
1School of Information Management and Engineering, Shanghai University of Finance and Economics, 200433 Shanghai, PR China.
概括
这项研究引入了一个新的内容和人气联合网络 (CPJN) 进行新闻推. 该CPJN模型有效地平衡了用户内容偏好和新闻受欢迎程度,以提高准确性.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 人工智能的人工智能
背景情况:
- 新闻推系统往往忽略了影响用户点击的双重因素:内容兴趣和新闻受欢迎程度.
- 现有的模型主要关注内容偏好,忽视了深入分析受欢迎程度驱动的参与和独立偏好建模.
研究的目的:
- 提出一个新的内容和人气联合网络 (CPJN) 模型,以加强新闻推.
- 独立地模拟用户对新闻内容和受欢迎程度的偏好,解决现有方法的局限性.
主要方法:
- 开发了一个CPJN模型,包括三个相互连接的网络:基于内容的,基于人气的和自适应的组合.
- 基于内容的网络消除了用户端信息中的受欢迎偏见,以改进内容偏好表示.
- 基于人气的网络从新闻侧信息中删除了内容偏见,以模拟人气偏好.
主要成果:
- 在两个真实世界数据集上,CPJN模型表现出了显著的有效性.
- 与最先进的基线相比,实现了1.493%的准确率和1.502%的召回率的平均改善.
结论:
- 拟议的CPJN模型成功地整合了用户内容和人气偏好,以获得更优质的新闻建议.
- 适应组合网络有效地处理不同用户对新闻受欢迎程度的敏感性,进一步提高推性能.
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