# Neural News Recommendation with Collaborative News Encoding and Structural User Encoding

arXiv: https://arxiv.org/abs/2109.00750 · HTML: https://mlsanity.com/paper/2109.00750/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View](https://mlsanity.com/paper/1909.03211/) (2019)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Neural News Recommendation with Attentive Multi-View Learning](https://mlsanity.com/paper/1907.05576/) (2019)
- [How Does Selective Mechanism Improve Self-Attention Networks?](https://mlsanity.com/paper/2005.00979/) (2020)

## Built on by (most cited first, 7 of 7)

- [LKPNR: LLM and KG for Personalized News Recommendation Framework](https://mlsanity.com/paper/2308.12028/) (2023)
- [DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://mlsanity.com/paper/2210.05196/) (2022)
- [JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer](https://mlsanity.com/paper/2404.04313/) (2024)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [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)

Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective representation learning of both news and users. Existing works typically encode news title and content separately while neglecting their semantic interaction, which is inadequate for news text comprehension. Besides, previous models encode user browsing history without leveraging the structural correlation of user browsed news to reflect user interests explicitly. In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning. CNE equipped with bidirectional LSTMs encodes news title and content collaboratively with cross-selection and cross-attention modules to learn semantic-interactive news representations. SUE utilizes graph convolutional networks to extract cluster-structural features of user history, followed by intra-cluster and inter-cluster attention modules to learn hierarchical user interest representations. Experiment results on the MIND dataset validate the effectiveness of our model to improve the performance of news recommendation. Our code is released at https://github.com/Veason-silverbullet/NNR.