Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

• URL: http://arxiv.org/abs/2307.06576v5
• Date: Tue, 26 Sep 2023 10:48:56 GMT
• Title: Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
• Authors: Boming Yang, Dairui Liu, Toyotaro Suzumura, Ruihai Dong, Irene Li
• Abstract summary: 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, which includes a global news graph and employs gated graph neural networks to enrich news representations. We extend this approach to a Global Candidate News, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation.
• Score: 12.783388192026855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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