Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users' hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations.

翻译：精确地向用户推荐候选新闻文章始终是个性化新闻推荐系统的核心挑战。近年来，大多数研究主要关注利用先进自然语言处理技术从丰富的文本数据中提取语义信息，采用基于本地历史新闻的内容驱动方法。然而，这种方法缺乏全局视角，未能考虑用户超越语义信息的隐藏动机和行为。为应对这一挑战，我们提出了一种名为GLORY（全局-局部新闻推荐系统）的新颖模型，该模型将其他用户学习到的全局表示与局部表示相结合，以增强个性化推荐系统。我们通过构建一个包含全局新闻图的全局感知历史新闻编码器来实现这一点，并使用门控图神经网络丰富新闻表示，进而通过历史新闻聚合器融合历史新闻表示。类似地，我们将此方法扩展到全局候选新闻编码器，利用全局实体图和候选新闻聚合器增强候选新闻表示。在两个公开新闻数据集上的评估结果表明，我们的方法优于现有方法。此外，我们的模型能提供更多样化的推荐。