# Graph Enhanced Representation Learning for News Recommendation

arXiv: https://arxiv.org/abs/2003.14292 · HTML: https://mlsanity.com/paper/2003.14292/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 30 of 30)

- [Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating](https://mlsanity.com/paper/2211.14065/) (2022)
- [Training Large-Scale News Recommenders with Pretrained Language Models in the Loop](https://mlsanity.com/paper/2102.09268/) (2021)
- [Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation](https://mlsanity.com/paper/2407.19944/) (2024)
- [Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations](https://mlsanity.com/paper/2307.06576/) (2023)
- [USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence](https://mlsanity.com/paper/2109.15012/) (2021)
- [Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://mlsanity.com/paper/2205.06058/) (2022)
- [Towards Disentangling Relevance and Bias in Unbiased Learning to Rank](https://mlsanity.com/paper/2212.13937/) (2022)
- [KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://mlsanity.com/paper/2302.12126/) (2023)
- [DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://mlsanity.com/paper/2210.05196/) (2022)
- [OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2504.17811/) (2025)
- [Aspect-driven User Preference and News Representation Learning for News Recommendation](https://mlsanity.com/paper/2110.05792/) (2021)
- [Personalized Visualization Recommendation](https://mlsanity.com/paper/2102.06343/) (2021)
- [Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective](https://mlsanity.com/paper/2105.12918/) (2021)
- [Neural News Recommendation with Event Extraction](https://mlsanity.com/paper/2111.05068/) (2021)
- [Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback](https://mlsanity.com/paper/2204.04397/) (2022)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [Graph Contrastive Learning on Multi-label Classification for Recommendations](https://mlsanity.com/paper/2501.06985/) (2025)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models](https://mlsanity.com/paper/2112.14370/) (2021)
- [Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation](https://mlsanity.com/paper/2207.14704/) (2022)
- [Graph Federated Learning with Hidden Representation Sharing](https://mlsanity.com/paper/2212.12158/) (2022)
- [Time Matters: Enhancing Pre-trained News Recommendation Models with Robust User Dwell Time Injection](https://mlsanity.com/paper/2405.12486/) (2024)
- [Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders](https://mlsanity.com/paper/2410.01470/) (2024)
- [Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling](https://mlsanity.com/paper/2504.14130/) (2025)
- [Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model](https://mlsanity.com/paper/2509.03527/) (2025)
- [IGNiteR: News Recommendation in Microblogging Applications (Extended Version)](https://mlsanity.com/paper/2210.01942/) (2022)
- [Towards a Theoretical Understanding of Two Tower Recommendation Models](https://mlsanity.com/paper/2403.00802/) (2024)
- [GLoCIM: Global-view Long Chain Interest Modeling for news recommendation](https://mlsanity.com/paper/2408.00859/) (2024)
- [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://mlsanity.com/paper/2508.13064/) (2025)
- [Modeling Stage-wise Evolution of User Interests for News Recommendation](https://mlsanity.com/paper/2603.10471/) (2026)

## Abstract (arXiv)

With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Existing news recommendation methods achieve personalization by building accurate news representations from news content and user representations from their direct interactions with news (e.g., click), while ignoring the high-order relatedness between users and news. Here we propose a news recommendation method which can enhance the representation learning of users and news by modeling their relatedness in a graph setting. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors. For news representations, a transformer architecture is first exploited to build news semantic representations. Then we combine it with the information from neighbor news in the graph via a graph attention network. For user representations, we not only represent users from their historically clicked news, but also attentively incorporate the representations of their neighbor users in the graph. Improved performances on a large-scale real-world dataset validate the effectiveness of our proposed method.